Overview

Dataset statistics

Number of variables71
Number of observations24583
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.5 MiB
Average record size in memory576.0 B

Variable types

DateTime4
Categorical44
Numeric18
Text5

Alerts

DT_GERACAO has constant value ""Constant
ANO_ELEICAO has constant value ""Constant
CD_TIPO_ELEICAO has constant value ""Constant
NM_TIPO_ELEICAO has constant value ""Constant
NR_TURNO has constant value ""Constant
TP_ABRANGENCIA has constant value ""Constant
CD_CARGO has constant value ""Constant
DS_CARGO has constant value ""Constant
NM_EMAIL has constant value ""Constant
TP_AGREMIACAO has constant value ""Constant
NR_FEDERACAO has constant value ""Constant
NM_FEDERACAO has constant value ""Constant
SG_FEDERACAO has constant value ""Constant
DS_COMPOSICAO_FEDERACAO has constant value ""Constant
NM_COLIGACAO has constant value ""Constant
CD_MUNICIPIO_NASCIMENTO has constant value ""Constant
NR_PROTOCOLO_CANDIDATURA has constant value ""Constant
SG_UE is highly overall correlated with SG_UFHigh correlation
SQ_CANDIDATO is highly overall correlated with SQ_COLIGACAO and 2 other fieldsHigh correlation
NR_CANDIDATO is highly overall correlated with NR_PARTIDO and 3 other fieldsHigh correlation
CD_DETALHE_SITUACAO_CAND is highly overall correlated with CD_SITUACAO_CANDIDATURA and 8 other fieldsHigh correlation
NR_PARTIDO is highly overall correlated with NR_CANDIDATO and 3 other fieldsHigh correlation
SQ_COLIGACAO is highly overall correlated with SQ_CANDIDATO and 2 other fieldsHigh correlation
NR_IDADE_DATA_POSSE is highly overall correlated with NR_TITULO_ELEITORAL_CANDIDATOHigh correlation
NR_TITULO_ELEITORAL_CANDIDATO is highly overall correlated with NR_IDADE_DATA_POSSEHigh correlation
CD_GRAU_INSTRUCAO is highly overall correlated with CD_NACIONALIDADE and 6 other fieldsHigh correlation
CD_ESTADO_CIVIL is highly overall correlated with CD_NACIONALIDADE and 6 other fieldsHigh correlation
CD_COR_RACA is highly overall correlated with CD_NACIONALIDADE and 6 other fieldsHigh correlation
CD_OCUPACAO is highly overall correlated with CD_NACIONALIDADE and 5 other fieldsHigh correlation
VR_DESPESA_MAX_CAMPANHA is highly overall correlated with CD_ELEICAO and 1 other fieldsHigh correlation
CD_SITUACAO_CANDIDATO_PLEITO is highly overall correlated with CD_SITUACAO_CANDIDATO_URNA and 12 other fieldsHigh correlation
CD_SITUACAO_CANDIDATO_URNA is highly overall correlated with CD_SITUACAO_CANDIDATO_PLEITO and 12 other fieldsHigh correlation
CD_SITUACAO_CANDIDATO_TOT is highly overall correlated with CD_SITUACAO_CANDIDATO_PLEITO and 12 other fieldsHigh correlation
CD_ELEICAO is highly overall correlated with VR_DESPESA_MAX_CAMPANHA and 1 other fieldsHigh correlation
DS_ELEICAO is highly overall correlated with VR_DESPESA_MAX_CAMPANHA and 1 other fieldsHigh correlation
SG_UF is highly overall correlated with SG_UE and 3 other fieldsHigh correlation
NM_SOCIAL_CANDIDATO is highly overall correlated with CD_NACIONALIDADE and 5 other fieldsHigh correlation
CD_SITUACAO_CANDIDATURA is highly overall correlated with CD_DETALHE_SITUACAO_CAND and 13 other fieldsHigh correlation
DS_SITUACAO_CANDIDATURA is highly overall correlated with CD_DETALHE_SITUACAO_CAND and 13 other fieldsHigh correlation
DS_DETALHE_SITUACAO_CAND is highly overall correlated with CD_DETALHE_SITUACAO_CAND and 11 other fieldsHigh correlation
SG_PARTIDO is highly overall correlated with NR_CANDIDATO and 3 other fieldsHigh correlation
NM_PARTIDO is highly overall correlated with NR_CANDIDATO and 3 other fieldsHigh correlation
DS_COMPOSICAO_COLIGACAO is highly overall correlated with NR_CANDIDATO and 3 other fieldsHigh correlation
CD_NACIONALIDADE is highly overall correlated with CD_GRAU_INSTRUCAO and 13 other fieldsHigh correlation
DS_NACIONALIDADE is highly overall correlated with CD_GRAU_INSTRUCAO and 13 other fieldsHigh correlation
SG_UF_NASCIMENTO is highly overall correlated with SQ_CANDIDATO and 8 other fieldsHigh correlation
CD_GENERO is highly overall correlated with CD_GRAU_INSTRUCAO and 13 other fieldsHigh correlation
DS_GENERO is highly overall correlated with CD_GRAU_INSTRUCAO and 13 other fieldsHigh correlation
DS_GRAU_INSTRUCAO is highly overall correlated with CD_GRAU_INSTRUCAO and 6 other fieldsHigh correlation
DS_ESTADO_CIVIL is highly overall correlated with CD_ESTADO_CIVIL and 6 other fieldsHigh correlation
DS_COR_RACA is highly overall correlated with CD_COR_RACA and 6 other fieldsHigh correlation
CD_SIT_TOT_TURNO is highly overall correlated with CD_SITUACAO_CANDIDATO_PLEITO and 11 other fieldsHigh correlation
DS_SIT_TOT_TURNO is highly overall correlated with CD_SITUACAO_CANDIDATO_PLEITO and 11 other fieldsHigh correlation
ST_REELEICAO is highly overall correlated with CD_GRAU_INSTRUCAO and 13 other fieldsHigh correlation
ST_DECLARAR_BENS is highly overall correlated with CD_GRAU_INSTRUCAO and 13 other fieldsHigh correlation
DS_SITUACAO_CANDIDATO_PLEITO is highly overall correlated with CD_DETALHE_SITUACAO_CAND and 13 other fieldsHigh correlation
DS_SITUACAO_CANDIDATO_URNA is highly overall correlated with CD_DETALHE_SITUACAO_CAND and 13 other fieldsHigh correlation
ST_CANDIDATO_INSERIDO_URNA is highly overall correlated with CD_DETALHE_SITUACAO_CAND and 13 other fieldsHigh correlation
NM_TIPO_DESTINACAO_VOTOS is highly overall correlated with CD_DETALHE_SITUACAO_CAND and 13 other fieldsHigh correlation
DS_SITUACAO_CANDIDATO_TOT is highly overall correlated with CD_DETALHE_SITUACAO_CAND and 13 other fieldsHigh correlation
ST_PREST_CONTAS is highly overall correlated with CD_DETALHE_SITUACAO_CAND and 13 other fieldsHigh correlation
CD_ELEICAO is highly imbalanced (85.0%)Imbalance
DS_ELEICAO is highly imbalanced (85.0%)Imbalance
SG_UF is highly imbalanced (55.1%)Imbalance
NM_SOCIAL_CANDIDATO is highly imbalanced (99.8%)Imbalance
CD_SITUACAO_CANDIDATURA is highly imbalanced (68.8%)Imbalance
DS_SITUACAO_CANDIDATURA is highly imbalanced (68.8%)Imbalance
DS_DETALHE_SITUACAO_CAND is highly imbalanced (87.2%)Imbalance
CD_NACIONALIDADE is highly imbalanced (97.6%)Imbalance
DS_NACIONALIDADE is highly imbalanced (97.6%)Imbalance
SG_UF_NASCIMENTO is highly imbalanced (64.0%)Imbalance
DS_COR_RACA is highly imbalanced (53.7%)Imbalance
ST_REELEICAO is highly imbalanced (87.5%)Imbalance
ST_DECLARAR_BENS is highly imbalanced (62.9%)Imbalance
DS_SITUACAO_CANDIDATO_PLEITO is highly imbalanced (86.2%)Imbalance
DS_SITUACAO_CANDIDATO_URNA is highly imbalanced (81.5%)Imbalance
ST_CANDIDATO_INSERIDO_URNA is highly imbalanced (77.4%)Imbalance
NM_TIPO_DESTINACAO_VOTOS is highly imbalanced (83.4%)Imbalance
DS_SITUACAO_CANDIDATO_TOT is highly imbalanced (85.5%)Imbalance
ST_PREST_CONTAS is highly imbalanced (72.8%)Imbalance
SQ_CANDIDATO has unique valuesUnique
NR_PROCESSO has unique valuesUnique

Reproduction

Analysis started2023-07-12 20:31:32.334865
Analysis finished2023-07-12 20:33:54.005203
Duration2 minutes and 21.67 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

DT_GERACAO
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
Minimum2023-06-05 00:00:00
Maximum2023-06-05 00:00:00
2023-07-12T17:33:54.227067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:54.443932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
Minimum2023-07-12 08:21:53
Maximum2023-07-12 08:25:58
2023-07-12T17:33:54.630817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:54.844684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

ANO_ELEICAO
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
2020
24583 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters98332
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 24583
100.0%

Length

2023-07-12T17:33:55.071546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:33:55.359550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2020 24583
100.0%

Most occurring characters

ValueCountFrequency (%)
2 49166
50.0%
0 49166
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98332
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 49166
50.0%
0 49166
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 98332
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 49166
50.0%
0 49166
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 49166
50.0%
0 49166
50.0%

CD_TIPO_ELEICAO
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
2
24583 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24583
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 24583
100.0%

Length

2023-07-12T17:33:55.552433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:33:55.789283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 24583
100.0%

Most occurring characters

ValueCountFrequency (%)
2 24583
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24583
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 24583
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24583
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 24583
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24583
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 24583
100.0%

NM_TIPO_ELEICAO
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
ELEIÇÃO ORDINÁRIA
24583 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters417911
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowELEIÇÃO ORDINÁRIA
2nd rowELEIÇÃO ORDINÁRIA
3rd rowELEIÇÃO ORDINÁRIA
4th rowELEIÇÃO ORDINÁRIA
5th rowELEIÇÃO ORDINÁRIA

Common Values

ValueCountFrequency (%)
ELEIÇÃO ORDINÁRIA 24583
100.0%

Length

2023-07-12T17:33:55.986961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:33:56.225811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
eleição 24583
50.0%
ordinária 24583
50.0%

Most occurring characters

ValueCountFrequency (%)
I 73749
17.6%
E 49166
11.8%
O 49166
11.8%
R 49166
11.8%
L 24583
 
5.9%
Ç 24583
 
5.9%
à 24583
 
5.9%
24583
 
5.9%
D 24583
 
5.9%
N 24583
 
5.9%
Other values (2) 49166
11.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 393328
94.1%
Space Separator 24583
 
5.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 73749
18.8%
E 49166
12.5%
O 49166
12.5%
R 49166
12.5%
L 24583
 
6.2%
Ç 24583
 
6.2%
à 24583
 
6.2%
D 24583
 
6.2%
N 24583
 
6.2%
Á 24583
 
6.2%
Space Separator
ValueCountFrequency (%)
24583
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 393328
94.1%
Common 24583
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 73749
18.8%
E 49166
12.5%
O 49166
12.5%
R 49166
12.5%
L 24583
 
6.2%
Ç 24583
 
6.2%
à 24583
 
6.2%
D 24583
 
6.2%
N 24583
 
6.2%
Á 24583
 
6.2%
Common
ValueCountFrequency (%)
24583
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 344162
82.4%
None 73749
 
17.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 73749
21.4%
E 49166
14.3%
O 49166
14.3%
R 49166
14.3%
L 24583
 
7.1%
24583
 
7.1%
D 24583
 
7.1%
N 24583
 
7.1%
A 24583
 
7.1%
None
ValueCountFrequency (%)
Ç 24583
33.3%
à 24583
33.3%
Á 24583
33.3%

NR_TURNO
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
1
24583 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24583
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 24583
100.0%

Length

2023-07-12T17:33:56.419692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:33:56.653552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 24583
100.0%

Most occurring characters

ValueCountFrequency (%)
1 24583
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24583
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 24583
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24583
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 24583
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24583
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 24583
100.0%

CD_ELEICAO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
426
24054 
445
 
529

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters73749
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row426
2nd row426
3rd row426
4th row426
5th row426

Common Values

ValueCountFrequency (%)
426 24054
97.8%
445 529
 
2.2%

Length

2023-07-12T17:33:56.849430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:33:57.088282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
426 24054
97.8%
445 529
 
2.2%

Most occurring characters

ValueCountFrequency (%)
4 25112
34.1%
2 24054
32.6%
6 24054
32.6%
5 529
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 73749
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 25112
34.1%
2 24054
32.6%
6 24054
32.6%
5 529
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 73749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 25112
34.1%
2 24054
32.6%
6 24054
32.6%
5 529
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 25112
34.1%
2 24054
32.6%
6 24054
32.6%
5 529
 
0.7%

DS_ELEICAO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
Eleições Municipais 2020
24054 
Eleições Municipais 2020 - AP
 
529

Length

Max length29
Median length24
Mean length24.107595
Min length24

Characters and Unicode

Total characters592637
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEleições Municipais 2020
2nd rowEleições Municipais 2020
3rd rowEleições Municipais 2020
4th rowEleições Municipais 2020
5th rowEleições Municipais 2020

Common Values

ValueCountFrequency (%)
Eleições Municipais 2020 24054
97.8%
Eleições Municipais 2020 - AP 529
 
2.2%

Length

2023-07-12T17:33:57.293156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:33:57.545001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
eleições 24583
32.9%
municipais 24583
32.9%
2020 24583
32.9%
529
 
0.7%
ap 529
 
0.7%

Most occurring characters

ValueCountFrequency (%)
i 98332
16.6%
50224
 
8.5%
e 49166
 
8.3%
0 49166
 
8.3%
2 49166
 
8.3%
s 49166
 
8.3%
E 24583
 
4.1%
c 24583
 
4.1%
a 24583
 
4.1%
p 24583
 
4.1%
Other values (9) 149085
25.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 393328
66.4%
Decimal Number 98332
 
16.6%
Space Separator 50224
 
8.5%
Uppercase Letter 50224
 
8.5%
Dash Punctuation 529
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 98332
25.0%
e 49166
12.5%
s 49166
12.5%
c 24583
 
6.2%
a 24583
 
6.2%
p 24583
 
6.2%
u 24583
 
6.2%
n 24583
 
6.2%
l 24583
 
6.2%
õ 24583
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
E 24583
48.9%
M 24583
48.9%
A 529
 
1.1%
P 529
 
1.1%
Decimal Number
ValueCountFrequency (%)
0 49166
50.0%
2 49166
50.0%
Space Separator
ValueCountFrequency (%)
50224
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 529
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 443552
74.8%
Common 149085
 
25.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 98332
22.2%
e 49166
11.1%
s 49166
11.1%
E 24583
 
5.5%
c 24583
 
5.5%
a 24583
 
5.5%
p 24583
 
5.5%
u 24583
 
5.5%
n 24583
 
5.5%
l 24583
 
5.5%
Other values (5) 74807
16.9%
Common
ValueCountFrequency (%)
50224
33.7%
0 49166
33.0%
2 49166
33.0%
- 529
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 543471
91.7%
None 49166
 
8.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 98332
18.1%
50224
9.2%
e 49166
9.0%
0 49166
9.0%
2 49166
9.0%
s 49166
9.0%
E 24583
 
4.5%
c 24583
 
4.5%
a 24583
 
4.5%
p 24583
 
4.5%
Other values (7) 99919
18.4%
None
ValueCountFrequency (%)
õ 24583
50.0%
ç 24583
50.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
Minimum2020-06-12 00:00:00
Maximum2020-11-15 00:00:00
2023-07-12T17:33:57.727886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:57.977966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

TP_ABRANGENCIA
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
MUNICIPAL
24583 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters221247
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMUNICIPAL
2nd rowMUNICIPAL
3rd rowMUNICIPAL
4th rowMUNICIPAL
5th rowMUNICIPAL

Common Values

ValueCountFrequency (%)
MUNICIPAL 24583
100.0%

Length

2023-07-12T17:33:58.240803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:33:58.464666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
municipal 24583
100.0%

Most occurring characters

ValueCountFrequency (%)
I 49166
22.2%
M 24583
11.1%
U 24583
11.1%
N 24583
11.1%
C 24583
11.1%
P 24583
11.1%
A 24583
11.1%
L 24583
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 221247
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 49166
22.2%
M 24583
11.1%
U 24583
11.1%
N 24583
11.1%
C 24583
11.1%
P 24583
11.1%
A 24583
11.1%
L 24583
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 221247
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 49166
22.2%
M 24583
11.1%
U 24583
11.1%
N 24583
11.1%
C 24583
11.1%
P 24583
11.1%
A 24583
11.1%
L 24583
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 221247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 49166
22.2%
M 24583
11.1%
U 24583
11.1%
N 24583
11.1%
C 24583
11.1%
P 24583
11.1%
A 24583
11.1%
L 24583
11.1%

SG_UF
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
PA
22280 
AP
2303 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters49166
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAP
2nd rowAP
3rd rowAP
4th rowAP
5th rowAP

Common Values

ValueCountFrequency (%)
PA 22280
90.6%
AP 2303
 
9.4%

Length

2023-07-12T17:33:58.874416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:33:59.106386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
pa 22280
90.6%
ap 2303
 
9.4%

Most occurring characters

ValueCountFrequency (%)
P 24583
50.0%
A 24583
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 49166
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 24583
50.0%
A 24583
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49166
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 24583
50.0%
A 24583
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 24583
50.0%
A 24583
50.0%

SG_UE
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4934.1483
Minimum4006
Maximum6173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.1 KiB
2023-07-12T17:33:59.367229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4006
5-th percentile4073
Q14332
median4774
Q35517
95-th percentile6076
Maximum6173
Range2167
Interquartile range (IQR)1185

Descriptive statistics

Standard deviation663.6483
Coefficient of variation (CV)0.13450109
Kurtosis-1.1872887
Mean4934.1483
Median Absolute Deviation (MAD)516
Skewness0.37534953
Sum1.2129617 × 108
Variance440429.07
MonotonicityNot monotonic
2023-07-12T17:33:59.671040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4278 1083
 
4.4%
4154 654
 
2.7%
6050 529
 
2.2%
5355 488
 
2.0%
4642 441
 
1.8%
5835 427
 
1.7%
4472 421
 
1.7%
4839 349
 
1.4%
4014 321
 
1.3%
6157 319
 
1.3%
Other values (150) 19551
79.5%
ValueCountFrequency (%)
4006 101
 
0.4%
4014 321
1.3%
4022 164
0.7%
4030 212
0.9%
4049 94
 
0.4%
4057 79
 
0.3%
4065 154
0.6%
4073 214
0.9%
4081 197
0.8%
4090 142
0.6%
ValueCountFrequency (%)
6173 95
 
0.4%
6157 319
1.3%
6130 196
0.8%
6122 134
0.5%
6114 83
 
0.3%
6106 81
 
0.3%
6092 154
0.6%
6084 82
 
0.3%
6076 120
 
0.5%
6068 66
 
0.3%

NM_UE
Text

Distinct160
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
2023-07-12T17:34:00.257895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length26
Median length22
Mean length10.652158
Min length4

Characters and Unicode

Total characters261862
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFERREIRA GOMES
2nd rowMAZAGÃO
3rd rowSERRA DO NAVIO
4th rowSERRA DO NAVIO
5th rowAMAPÁ
ValueCountFrequency (%)
do 4455
 
10.8%
pará 1388
 
3.4%
são 1377
 
3.3%
belém 1083
 
2.6%
araguaia 769
 
1.9%
santa 758
 
1.8%
ananindeua 654
 
1.6%
santarém 563
 
1.4%
de 560
 
1.4%
macapá 529
 
1.3%
Other values (188) 29104
70.6%
2023-07-12T17:34:01.156708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 45000
17.2%
R 20707
 
7.9%
O 19875
 
7.6%
I 16841
 
6.4%
16657
 
6.4%
N 14677
 
5.6%
E 14106
 
5.4%
U 12751
 
4.9%
S 11279
 
4.3%
T 10649
 
4.1%
Other values (26) 79320
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 244474
93.4%
Space Separator 16657
 
6.4%
Dash Punctuation 655
 
0.3%
Other Punctuation 76
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 45000
18.4%
R 20707
 
8.5%
O 19875
 
8.1%
I 16841
 
6.9%
N 14677
 
6.0%
E 14106
 
5.8%
U 12751
 
5.2%
S 11279
 
4.6%
T 10649
 
4.4%
D 9519
 
3.9%
Other values (23) 69070
28.3%
Space Separator
ValueCountFrequency (%)
16657
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 655
100.0%
Other Punctuation
ValueCountFrequency (%)
' 76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 244474
93.4%
Common 17388
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 45000
18.4%
R 20707
 
8.5%
O 19875
 
8.1%
I 16841
 
6.9%
N 14677
 
6.0%
E 14106
 
5.8%
U 12751
 
5.2%
S 11279
 
4.6%
T 10649
 
4.4%
D 9519
 
3.9%
Other values (23) 69070
28.3%
Common
ValueCountFrequency (%)
16657
95.8%
- 655
 
3.8%
' 76
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 246422
94.1%
None 15440
 
5.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 45000
18.3%
R 20707
 
8.4%
O 19875
 
8.1%
I 16841
 
6.8%
16657
 
6.8%
N 14677
 
6.0%
E 14106
 
5.7%
U 12751
 
5.2%
S 11279
 
4.6%
T 10649
 
4.3%
Other values (16) 63880
25.9%
None
ValueCountFrequency (%)
Á 5218
33.8%
à 3503
22.7%
É 2700
17.5%
Ç 1590
 
10.3%
Ó 1043
 
6.8%
Í 484
 
3.1%
 450
 
2.9%
Ô 193
 
1.2%
Ê 181
 
1.2%
Ú 78
 
0.5%

CD_CARGO
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
13
24583 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters49166
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
13 24583
100.0%

Length

2023-07-12T17:34:01.500494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:01.787317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
13 24583
100.0%

Most occurring characters

ValueCountFrequency (%)
1 24583
50.0%
3 24583
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49166
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 24583
50.0%
3 24583
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49166
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 24583
50.0%
3 24583
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 24583
50.0%
3 24583
50.0%

DS_CARGO
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
VEREADOR
24583 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters196664
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVEREADOR
2nd rowVEREADOR
3rd rowVEREADOR
4th rowVEREADOR
5th rowVEREADOR

Common Values

ValueCountFrequency (%)
VEREADOR 24583
100.0%

Length

2023-07-12T17:34:02.017184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:02.302000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
vereador 24583
100.0%

Most occurring characters

ValueCountFrequency (%)
E 49166
25.0%
R 49166
25.0%
V 24583
12.5%
A 24583
12.5%
D 24583
12.5%
O 24583
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 196664
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 49166
25.0%
R 49166
25.0%
V 24583
12.5%
A 24583
12.5%
D 24583
12.5%
O 24583
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 196664
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 49166
25.0%
R 49166
25.0%
V 24583
12.5%
A 24583
12.5%
D 24583
12.5%
O 24583
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 196664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 49166
25.0%
R 49166
25.0%
V 24583
12.5%
A 24583
12.5%
D 24583
12.5%
O 24583
12.5%

SQ_CANDIDATO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24583
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2969587 × 1011
Minimum3.0000633 × 1010
Maximum1.4000128 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.1 KiB
2023-07-12T17:34:02.595819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.0000633 × 1010
5-th percentile3.000096 × 1010
Q11.4000075 × 1011
median1.4000094 × 1011
Q31.4000112 × 1011
95-th percentile1.4000125 × 1011
Maximum1.4000128 × 1011
Range1.1000065 × 1011
Interquartile range (IQR)365279

Descriptive statistics

Standard deviation3.20532 × 1010
Coefficient of variation (CV)0.24714125
Kurtosis5.7791235
Mean1.2969587 × 1011
Median Absolute Deviation (MAD)183527
Skewness-2.7890237
Sum3.1883137 × 1015
Variance1.0274076 × 1021
MonotonicityNot monotonic
2023-07-12T17:34:02.990576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.000067705 × 10101
 
< 0.1%
1.400012265 × 10111
 
< 0.1%
1.400009807 × 10111
 
< 0.1%
1.400006585 × 10111
 
< 0.1%
1.40000739 × 10111
 
< 0.1%
1.40001025 × 10111
 
< 0.1%
1.40001074 × 10111
 
< 0.1%
1.400006733 × 10111
 
< 0.1%
1.40000891 × 10111
 
< 0.1%
1.400007401 × 10111
 
< 0.1%
Other values (24573) 24573
> 99.9%
ValueCountFrequency (%)
3.000063285 × 10101
< 0.1%
3.000063285 × 10101
< 0.1%
3.000063285 × 10101
< 0.1%
3.000063285 × 10101
< 0.1%
3.000063285 × 10101
< 0.1%
3.000063286 × 10101
< 0.1%
3.000063286 × 10101
< 0.1%
3.000063286 × 10101
< 0.1%
3.000063286 × 10101
< 0.1%
3.000063286 × 10101
< 0.1%
ValueCountFrequency (%)
1.400012788 × 10111
< 0.1%
1.400012768 × 10111
< 0.1%
1.400012762 × 10111
< 0.1%
1.400012752 × 10111
< 0.1%
1.400012752 × 10111
< 0.1%
1.400012752 × 10111
< 0.1%
1.400012748 × 10111
< 0.1%
1.400012748 × 10111
< 0.1%
1.400012748 × 10111
< 0.1%
1.400012748 × 10111
< 0.1%

NR_CANDIDATO
Real number (ℝ)

HIGH CORRELATION 

Distinct4646
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31086.055
Minimum10000
Maximum90999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.1 KiB
2023-07-12T17:34:03.379337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile10666
Q115015
median22300
Q345133
95-th percentile77114
Maximum90999
Range80999
Interquartile range (IQR)30118

Descriptive statistics

Standard deviation20829.577
Coefficient of variation (CV)0.67006176
Kurtosis0.48032757
Mean31086.055
Median Absolute Deviation (MAD)9300
Skewness1.1570288
Sum7.6418849 × 108
Variance4.3387127 × 108
MonotonicityNot monotonic
2023-07-12T17:34:03.696436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15123 144
 
0.6%
15000 137
 
0.6%
15555 137
 
0.6%
15111 116
 
0.5%
15222 115
 
0.5%
55123 112
 
0.5%
22222 112
 
0.5%
20000 111
 
0.5%
20123 111
 
0.5%
13123 110
 
0.4%
Other values (4636) 23378
95.1%
ValueCountFrequency (%)
10000 85
0.3%
10001 15
 
0.1%
10002 1
 
< 0.1%
10003 1
 
< 0.1%
10005 2
 
< 0.1%
10006 1
 
< 0.1%
10007 24
 
0.1%
10008 1
 
< 0.1%
10009 1
 
< 0.1%
10010 26
 
0.1%
ValueCountFrequency (%)
90999 37
0.2%
90920 1
 
< 0.1%
90909 1
 
< 0.1%
90901 3
 
< 0.1%
90900 9
 
< 0.1%
90890 1
 
< 0.1%
90888 26
0.1%
90820 1
 
< 0.1%
90815 1
 
< 0.1%
90800 5
 
< 0.1%
Distinct24472
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
2023-07-12T17:34:04.211121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length65
Median length46
Mean length25.569133
Min length9

Characters and Unicode

Total characters628566
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24371 ?
Unique (%)99.1%

Sample

1st rowRAIMUNDO MESQUITA FERREIRA DOS SANTOS
2nd rowMAURICIO DEL CASTILLO RAIOL
3rd rowKENNAS DE OLIVEIRA DOS SANTOS
4th rowYAGO NORATO SALES DA SILVA
5th rowMAURÍCIO DE OLIVEIRA SUCUPIRA
ValueCountFrequency (%)
de 6171
 
6.3%
da 5435
 
5.5%
silva 5265
 
5.3%
santos 3006
 
3.1%
dos 2512
 
2.6%
maria 1687
 
1.7%
oliveira 1663
 
1.7%
souza 1421
 
1.4%
costa 1340
 
1.4%
sousa 1262
 
1.3%
Other values (10112) 68712
69.8%
2023-07-12T17:34:05.039608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 83718
13.3%
73897
11.8%
O 54644
8.7%
E 53927
8.6%
I 50169
8.0%
S 47668
 
7.6%
R 44904
 
7.1%
N 32637
 
5.2%
L 31160
 
5.0%
D 30589
 
4.9%
Other values (30) 125253
19.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 554668
88.2%
Space Separator 73897
 
11.8%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 83718
15.1%
O 54644
9.9%
E 53927
9.7%
I 50169
9.0%
S 47668
8.6%
R 44904
8.1%
N 32637
 
5.9%
L 31160
 
5.6%
D 30589
 
5.5%
T 16872
 
3.0%
Other values (28) 108380
19.5%
Space Separator
ValueCountFrequency (%)
73897
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 554668
88.2%
Common 73898
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 83718
15.1%
O 54644
9.9%
E 53927
9.7%
I 50169
9.0%
S 47668
8.6%
R 44904
8.1%
N 32637
 
5.9%
L 31160
 
5.6%
D 30589
 
5.5%
T 16872
 
3.0%
Other values (28) 108380
19.5%
Common
ValueCountFrequency (%)
73897
> 99.9%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 623905
99.3%
None 4661
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 83718
13.4%
73897
11.8%
O 54644
8.8%
E 53927
8.6%
I 50169
8.0%
S 47668
 
7.6%
R 44904
 
7.2%
N 32637
 
5.2%
L 31160
 
5.0%
D 30589
 
4.9%
Other values (18) 120592
19.3%
None
ValueCountFrequency (%)
à 1360
29.2%
É 1100
23.6%
Ç 957
20.5%
Á 360
 
7.7%
Ú 296
 
6.4%
Ô 183
 
3.9%
Í 166
 
3.6%
Ê 104
 
2.2%
Ó 61
 
1.3%
 59
 
1.3%
Other values (2) 15
 
0.3%
Distinct22565
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
2023-07-12T17:34:05.518313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length30
Median length25
Mean length13.000041
Min length2

Characters and Unicode

Total characters319580
Distinct characters56
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21266 ?
Unique (%)86.5%

Sample

1st rowGOIABA
2nd rowMAURICIO DA SAÚDE
3rd rowNEGUINHO DO TAXI
4th rowYAGO SALES
5th rowMAURÍCIO SUCUPIRA
ValueCountFrequency (%)
do 2044
 
4.0%
da 1497
 
3.0%
professor 595
 
1.2%
professora 550
 
1.1%
santos 415
 
0.8%
silva 347
 
0.7%
prof 315
 
0.6%
maria 300
 
0.6%
de 284
 
0.6%
pastor 257
 
0.5%
Other values (12196) 43998
86.9%
2023-07-12T17:34:06.312823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 42387
13.3%
O 30722
 
9.6%
26214
 
8.2%
E 25894
 
8.1%
I 25548
 
8.0%
R 25084
 
7.8%
N 19117
 
6.0%
S 17355
 
5.4%
L 16056
 
5.0%
D 13892
 
4.3%
Other values (46) 77311
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 293020
91.7%
Space Separator 26214
 
8.2%
Other Punctuation 170
 
0.1%
Decimal Number 131
 
< 0.1%
Dash Punctuation 18
 
< 0.1%
Open Punctuation 12
 
< 0.1%
Close Punctuation 12
 
< 0.1%
Modifier Symbol 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 42387
14.5%
O 30722
10.5%
E 25894
 
8.8%
I 25548
 
8.7%
R 25084
 
8.6%
N 19117
 
6.5%
S 17355
 
5.9%
L 16056
 
5.5%
D 13892
 
4.7%
C 9557
 
3.3%
Other values (29) 67408
23.0%
Decimal Number
ValueCountFrequency (%)
1 32
24.4%
0 30
22.9%
3 17
13.0%
2 16
12.2%
5 11
 
8.4%
8 8
 
6.1%
7 7
 
5.3%
4 5
 
3.8%
9 3
 
2.3%
6 2
 
1.5%
Space Separator
ValueCountFrequency (%)
26214
100.0%
Other Punctuation
ValueCountFrequency (%)
. 170
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 293020
91.7%
Common 26560
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 42387
14.5%
O 30722
10.5%
E 25894
 
8.8%
I 25548
 
8.7%
R 25084
 
8.6%
N 19117
 
6.5%
S 17355
 
5.9%
L 16056
 
5.5%
D 13892
 
4.7%
C 9557
 
3.3%
Other values (29) 67408
23.0%
Common
ValueCountFrequency (%)
26214
98.7%
. 170
 
0.6%
1 32
 
0.1%
0 30
 
0.1%
- 18
 
0.1%
3 17
 
0.1%
2 16
 
0.1%
( 12
 
< 0.1%
) 12
 
< 0.1%
5 11
 
< 0.1%
Other values (7) 28
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314659
98.5%
None 4921
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 42387
13.5%
O 30722
9.8%
26214
 
8.3%
E 25894
 
8.2%
I 25548
 
8.1%
R 25084
 
8.0%
N 19117
 
6.1%
S 17355
 
5.5%
L 16056
 
5.1%
D 13892
 
4.4%
Other values (32) 72390
23.0%
None
ValueCountFrequency (%)
à 1729
35.1%
É 852
17.3%
Á 657
 
13.4%
Ç 472
 
9.6%
Ú 346
 
7.0%
Í 213
 
4.3%
Ó 209
 
4.2%
Ô 157
 
3.2%
Ê 151
 
3.1%
 104
 
2.1%
Other values (4) 31
 
0.6%

NM_SOCIAL_CANDIDATO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
#NULO#
24574 
NÃO DIVULGÁVEL
 
1
HAGATA MATOS DA SILVA
 
1
SUZZY KAMEG SERRÃO GEMAQUE
 
1
PRISCILLA SILVA BRITO
 
1
Other values (5)
 
5

Length

Max length29
Median length6
Mean length6.0061831
Min length6

Characters and Unicode

Total characters147650
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row#NULO#
2nd row#NULO#
3rd row#NULO#
4th row#NULO#
5th row#NULO#

Common Values

ValueCountFrequency (%)
#NULO# 24574
> 99.9%
NÃO DIVULGÁVEL 1
 
< 0.1%
HAGATA MATOS DA SILVA 1
 
< 0.1%
SUZZY KAMEG SERRÃO GEMAQUE 1
 
< 0.1%
PRISCILLA SILVA BRITO 1
 
< 0.1%
SHAYLLA RODRIGUES DOS SANTOS 1
 
< 0.1%
PAULA BULCÃO DE ARAUJO 1
 
< 0.1%
DJENANNY PEREIRA 1
 
< 0.1%
MARIA EDUARDA SANTOS PINHEIRO 1
 
< 0.1%
RAYSSA RAYANA ALMEIDA RIBEIRO 1
 
< 0.1%

Length

2023-07-12T17:34:06.611640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:06.928443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nulo 24574
99.9%
santos 2
 
< 0.1%
silva 2
 
< 0.1%
almeida 1
 
< 0.1%
rayana 1
 
< 0.1%
rayssa 1
 
< 0.1%
pinheiro 1
 
< 0.1%
eduarda 1
 
< 0.1%
maria 1
 
< 0.1%
pereira 1
 
< 0.1%
Other values (20) 20
 
0.1%

Most occurring characters

ValueCountFrequency (%)
# 49148
33.3%
O 24586
16.7%
L 24585
16.7%
N 24582
16.6%
U 24582
16.6%
A 31
 
< 0.1%
22
 
< 0.1%
R 16
 
< 0.1%
S 15
 
< 0.1%
I 14
 
< 0.1%
Other values (17) 69
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 98480
66.7%
Other Punctuation 49148
33.3%
Space Separator 22
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 24586
25.0%
L 24585
25.0%
N 24582
25.0%
U 24582
25.0%
A 31
 
< 0.1%
R 16
 
< 0.1%
S 15
 
< 0.1%
I 14
 
< 0.1%
E 14
 
< 0.1%
D 9
 
< 0.1%
Other values (15) 46
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
# 49148
100.0%
Space Separator
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 98480
66.7%
Common 49170
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 24586
25.0%
L 24585
25.0%
N 24582
25.0%
U 24582
25.0%
A 31
 
< 0.1%
R 16
 
< 0.1%
S 15
 
< 0.1%
I 14
 
< 0.1%
E 14
 
< 0.1%
D 9
 
< 0.1%
Other values (15) 46
 
< 0.1%
Common
ValueCountFrequency (%)
# 49148
> 99.9%
22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147646
> 99.9%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
# 49148
33.3%
O 24586
16.7%
L 24585
16.7%
N 24582
16.6%
U 24582
16.6%
A 31
 
< 0.1%
22
 
< 0.1%
R 16
 
< 0.1%
S 15
 
< 0.1%
I 14
 
< 0.1%
Other values (15) 65
 
< 0.1%
None
ValueCountFrequency (%)
à 3
75.0%
Á 1
 
25.0%

NR_CPF_CANDIDATO
Real number (ℝ)

Distinct24556
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7806484 × 1010
Minimum-4
Maximum9.9983753 × 1010
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size384.1 KiB
2023-07-12T17:34:07.281226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile8.5849795 × 108
Q11.9558345 × 1010
median5.1628244 × 1010
Q37.3799226 × 1010
95-th percentile9.3328992 × 1010
Maximum9.9983753 × 1010
Range9.9983753 × 1010
Interquartile range (IQR)5.4240881 × 1010

Descriptive statistics

Standard deviation3.1046492 × 1010
Coefficient of variation (CV)0.64942011
Kurtosis-1.2882624
Mean4.7806484 × 1010
Median Absolute Deviation (MAD)2.5858478 × 1010
Skewness-0.14832976
Sum1.1752268 × 1015
Variance9.6388467 × 1020
MonotonicityNot monotonic
2023-07-12T17:34:07.603916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.362901021 × 10102
 
< 0.1%
9.791208522 × 10102
 
< 0.1%
7.500367822 × 10102
 
< 0.1%
6.547957225 × 10102
 
< 0.1%
8.034346527 × 10102
 
< 0.1%
6.977034425 × 10102
 
< 0.1%
1.063952573 × 10102
 
< 0.1%
7.11409722 × 10102
 
< 0.1%
9.676367125 × 10102
 
< 0.1%
3.317517922 × 10102
 
< 0.1%
Other values (24546) 24563
99.9%
ValueCountFrequency (%)
-4 1
< 0.1%
1153200 1
< 0.1%
1865200 1
< 0.1%
2629194 1
< 0.1%
3014266 1
< 0.1%
3732223 1
< 0.1%
4747208 1
< 0.1%
4955226 1
< 0.1%
7914237 1
< 0.1%
8986274 1
< 0.1%
ValueCountFrequency (%)
9.998375339 × 10101
< 0.1%
9.997100329 × 10101
< 0.1%
9.99653482 × 10101
< 0.1%
9.994945823 × 10101
< 0.1%
9.994880021 × 10101
< 0.1%
9.994432427 × 10101
< 0.1%
9.99390452 × 10101
< 0.1%
9.99377782 × 10101
< 0.1%
9.993544922 × 10101
< 0.1%
9.993324125 × 10101
< 0.1%

NM_EMAIL
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
NÃO DIVULGÁVEL
24583 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters344162
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNÃO DIVULGÁVEL
2nd rowNÃO DIVULGÁVEL
3rd rowNÃO DIVULGÁVEL
4th rowNÃO DIVULGÁVEL
5th rowNÃO DIVULGÁVEL

Common Values

ValueCountFrequency (%)
NÃO DIVULGÁVEL 24583
100.0%

Length

2023-07-12T17:34:07.885743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:08.114758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
não 24583
50.0%
divulgável 24583
50.0%

Most occurring characters

ValueCountFrequency (%)
V 49166
14.3%
L 49166
14.3%
N 24583
7.1%
à 24583
7.1%
O 24583
7.1%
24583
7.1%
D 24583
7.1%
I 24583
7.1%
U 24583
7.1%
G 24583
7.1%
Other values (2) 49166
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 319579
92.9%
Space Separator 24583
 
7.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
V 49166
15.4%
L 49166
15.4%
N 24583
7.7%
à 24583
7.7%
O 24583
7.7%
D 24583
7.7%
I 24583
7.7%
U 24583
7.7%
G 24583
7.7%
Á 24583
7.7%
Space Separator
ValueCountFrequency (%)
24583
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 319579
92.9%
Common 24583
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
V 49166
15.4%
L 49166
15.4%
N 24583
7.7%
à 24583
7.7%
O 24583
7.7%
D 24583
7.7%
I 24583
7.7%
U 24583
7.7%
G 24583
7.7%
Á 24583
7.7%
Common
ValueCountFrequency (%)
24583
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 294996
85.7%
None 49166
 
14.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
V 49166
16.7%
L 49166
16.7%
N 24583
8.3%
O 24583
8.3%
24583
8.3%
D 24583
8.3%
I 24583
8.3%
U 24583
8.3%
G 24583
8.3%
E 24583
8.3%
None
ValueCountFrequency (%)
à 24583
50.0%
Á 24583
50.0%

CD_SITUACAO_CANDIDATURA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
12
23203 
3
 
1380

Length

Max length2
Median length2
Mean length1.9438636
Min length1

Characters and Unicode

Total characters47786
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row12
3rd row12
4th row12
5th row12

Common Values

ValueCountFrequency (%)
12 23203
94.4%
3 1380
 
5.6%

Length

2023-07-12T17:34:08.309639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:08.540172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
12 23203
94.4%
3 1380
 
5.6%

Most occurring characters

ValueCountFrequency (%)
1 23203
48.6%
2 23203
48.6%
3 1380
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47786
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 23203
48.6%
2 23203
48.6%
3 1380
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 47786
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 23203
48.6%
2 23203
48.6%
3 1380
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 23203
48.6%
2 23203
48.6%
3 1380
 
2.9%

DS_SITUACAO_CANDIDATURA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
APTO
23203 
INAPTO
 
1380

Length

Max length6
Median length4
Mean length4.1122727
Min length4

Characters and Unicode

Total characters101092
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAPTO
2nd rowAPTO
3rd rowAPTO
4th rowAPTO
5th rowAPTO

Common Values

ValueCountFrequency (%)
APTO 23203
94.4%
INAPTO 1380
 
5.6%

Length

2023-07-12T17:34:08.757040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:09.023875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
apto 23203
94.4%
inapto 1380
 
5.6%

Most occurring characters

ValueCountFrequency (%)
A 24583
24.3%
P 24583
24.3%
T 24583
24.3%
O 24583
24.3%
I 1380
 
1.4%
N 1380
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 101092
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 24583
24.3%
P 24583
24.3%
T 24583
24.3%
O 24583
24.3%
I 1380
 
1.4%
N 1380
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 101092
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 24583
24.3%
P 24583
24.3%
T 24583
24.3%
O 24583
24.3%
I 1380
 
1.4%
N 1380
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101092
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 24583
24.3%
P 24583
24.3%
T 24583
24.3%
O 24583
24.3%
I 1380
 
1.4%
N 1380
 
1.4%

CD_DETALHE_SITUACAO_CAND
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5679941
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.1 KiB
2023-07-12T17:34:09.221192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2
Q32
95-th percentile6
Maximum20
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4155924
Coefficient of variation (CV)0.94065338
Kurtosis17.619414
Mean2.5679941
Median Absolute Deviation (MAD)0
Skewness4.3470912
Sum63129
Variance5.8350866
MonotonicityNot monotonic
2023-07-12T17:34:09.442059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 23062
93.8%
14 887
 
3.6%
6 334
 
1.4%
10 140
 
0.6%
4 106
 
0.4%
16 27
 
0.1%
13 9
 
< 0.1%
5 7
 
< 0.1%
20 6
 
< 0.1%
7 3
 
< 0.1%
ValueCountFrequency (%)
2 23062
93.8%
4 106
 
0.4%
5 7
 
< 0.1%
6 334
 
1.4%
7 3
 
< 0.1%
10 140
 
0.6%
13 9
 
< 0.1%
14 887
 
3.6%
16 27
 
0.1%
17 2
 
< 0.1%
ValueCountFrequency (%)
20 6
 
< 0.1%
17 2
 
< 0.1%
16 27
 
0.1%
14 887
3.6%
13 9
 
< 0.1%
10 140
 
0.6%
7 3
 
< 0.1%
6 334
 
1.4%
5 7
 
< 0.1%
4 106
 
0.4%

DS_DETALHE_SITUACAO_CAND
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
DEFERIDO
23062 
INDEFERIDO
 
887
RENÚNCIA
 
334
CASSADO
 
140
INDEFERIDO COM RECURSO
 
106
Other values (6)
 
54

Length

Max length32
Median length8
Mean length8.1516902
Min length7

Characters and Unicode

Total characters200393
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEFERIDO
2nd rowDEFERIDO
3rd rowDEFERIDO
4th rowINDEFERIDO COM RECURSO
5th rowDEFERIDO

Common Values

ValueCountFrequency (%)
DEFERIDO 23062
93.8%
INDEFERIDO 887
 
3.6%
RENÚNCIA 334
 
1.4%
CASSADO 140
 
0.6%
INDEFERIDO COM RECURSO 106
 
0.4%
DEFERIDO COM RECURSO 27
 
0.1%
PEDIDO NÃO CONHECIDO 9
 
< 0.1%
CANCELADO 7
 
< 0.1%
PEDIDO NÃO CONHECIDO COM RECURSO 6
 
< 0.1%
FALECIDO 3
 
< 0.1%

Length

2023-07-12T17:34:09.709891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
deferido 23089
92.7%
indeferido 993
 
4.0%
renúncia 334
 
1.3%
cassado 140
 
0.6%
com 139
 
0.6%
recurso 139
 
0.6%
pedido 15
 
0.1%
não 15
 
0.1%
conhecido 15
 
0.1%
cancelado 7
 
< 0.1%
Other values (4) 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 48687
24.3%
D 48363
24.1%
I 25442
12.7%
R 24694
12.3%
O 24572
12.3%
F 24085
12.0%
N 1704
 
0.9%
C 799
 
0.4%
A 633
 
0.3%
S 419
 
0.2%
Other values (11) 995
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 200081
99.8%
Space Separator 312
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 48687
24.3%
D 48363
24.2%
I 25442
12.7%
R 24694
12.3%
O 24572
12.3%
F 24085
12.0%
N 1704
 
0.9%
C 799
 
0.4%
A 633
 
0.3%
S 419
 
0.2%
Other values (10) 683
 
0.3%
Space Separator
ValueCountFrequency (%)
312
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 200081
99.8%
Common 312
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 48687
24.3%
D 48363
24.2%
I 25442
12.7%
R 24694
12.3%
O 24572
12.3%
F 24085
12.0%
N 1704
 
0.9%
C 799
 
0.4%
A 633
 
0.3%
S 419
 
0.2%
Other values (10) 683
 
0.3%
Common
ValueCountFrequency (%)
312
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200044
99.8%
None 349
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 48687
24.3%
D 48363
24.2%
I 25442
12.7%
R 24694
12.3%
O 24572
12.3%
F 24085
12.0%
N 1704
 
0.9%
C 799
 
0.4%
A 633
 
0.3%
S 419
 
0.2%
Other values (9) 646
 
0.3%
None
ValueCountFrequency (%)
Ú 334
95.7%
à 15
 
4.3%

TP_AGREMIACAO
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
PARTIDO ISOLADO
24583 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters368745
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARTIDO ISOLADO
2nd rowPARTIDO ISOLADO
3rd rowPARTIDO ISOLADO
4th rowPARTIDO ISOLADO
5th rowPARTIDO ISOLADO

Common Values

ValueCountFrequency (%)
PARTIDO ISOLADO 24583
100.0%

Length

2023-07-12T17:34:09.948743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:10.173605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
partido 24583
50.0%
isolado 24583
50.0%

Most occurring characters

ValueCountFrequency (%)
O 73749
20.0%
A 49166
13.3%
I 49166
13.3%
D 49166
13.3%
P 24583
 
6.7%
R 24583
 
6.7%
T 24583
 
6.7%
24583
 
6.7%
S 24583
 
6.7%
L 24583
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 344162
93.3%
Space Separator 24583
 
6.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 73749
21.4%
A 49166
14.3%
I 49166
14.3%
D 49166
14.3%
P 24583
 
7.1%
R 24583
 
7.1%
T 24583
 
7.1%
S 24583
 
7.1%
L 24583
 
7.1%
Space Separator
ValueCountFrequency (%)
24583
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 344162
93.3%
Common 24583
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 73749
21.4%
A 49166
14.3%
I 49166
14.3%
D 49166
14.3%
P 24583
 
7.1%
R 24583
 
7.1%
T 24583
 
7.1%
S 24583
 
7.1%
L 24583
 
7.1%
Common
ValueCountFrequency (%)
24583
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 368745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 73749
20.0%
A 49166
13.3%
I 49166
13.3%
D 49166
13.3%
P 24583
 
6.7%
R 24583
 
6.7%
T 24583
 
6.7%
24583
 
6.7%
S 24583
 
6.7%
L 24583
 
6.7%

NR_PARTIDO
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.742993
Minimum10
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.1 KiB
2023-07-12T17:34:10.369486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q115
median22
Q345
95-th percentile77
Maximum90
Range80
Interquartile range (IQR)30

Descriptive statistics

Standard deviation20.815323
Coefficient of variation (CV)0.67707537
Kurtosis0.48088685
Mean30.742993
Median Absolute Deviation (MAD)9
Skewness1.1577231
Sum755755
Variance433.27769
MonotonicityNot monotonic
2023-07-12T17:34:10.635323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
15 2234
 
9.1%
20 1700
 
6.9%
22 1669
 
6.8%
55 1613
 
6.6%
45 1539
 
6.3%
13 1473
 
6.0%
10 1462
 
5.9%
25 1246
 
5.1%
12 1229
 
5.0%
40 1171
 
4.8%
Other values (21) 9247
37.6%
ValueCountFrequency (%)
10 1462
5.9%
11 752
 
3.1%
12 1229
5.0%
13 1473
6.0%
14 1025
4.2%
15 2234
9.1%
16 3
 
< 0.1%
17 523
 
2.1%
18 203
 
0.8%
19 839
 
3.4%
ValueCountFrequency (%)
90 762
3.1%
80 2
 
< 0.1%
77 594
 
2.4%
70 626
 
2.5%
65 488
 
2.0%
55 1613
6.6%
51 638
 
2.6%
50 364
 
1.5%
45 1539
6.3%
43 461
 
1.9%

SG_PARTIDO
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
MDB
2234 
PSC
1700 
PL
1669 
PSD
1613 
PSDB
 
1539
Other values (26)
15828 

Length

Max length13
Median length12
Mean length4.2271488
Min length2

Characters and Unicode

Total characters103916
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPP
2nd rowPT
3rd rowPSD
4th rowAVANTE
5th rowPDT

Common Values

ValueCountFrequency (%)
MDB 2234
 
9.1%
PSC 1700
 
6.9%
PL 1669
 
6.8%
PSD 1613
 
6.6%
PSDB 1539
 
6.3%
PT 1473
 
6.0%
REPUBLICANOS 1462
 
5.9%
DEM 1246
 
5.1%
PDT 1229
 
5.0%
PSB 1171
 
4.8%
Other values (21) 9247
37.6%

Length

2023-07-12T17:34:10.928141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mdb 2234
 
8.7%
psc 1700
 
6.7%
pl 1669
 
6.5%
psd 1613
 
6.3%
psdb 1539
 
6.0%
pt 1473
 
5.8%
republicanos 1462
 
5.7%
dem 1246
 
4.9%
pdt 1229
 
4.8%
psb 1171
 
4.6%
Other values (23) 10223
40.0%

Most occurring characters

ValueCountFrequency (%)
P 19503
18.8%
D 12341
11.9%
S 9731
9.4%
B 8510
8.2%
A 7383
 
7.1%
T 6416
 
6.2%
E 5767
 
5.5%
C 4891
 
4.7%
I 4758
 
4.6%
O 4675
 
4.5%
Other values (9) 19941
19.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 101964
98.1%
Space Separator 976
 
0.9%
Lowercase Letter 976
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 19503
19.1%
D 12341
12.1%
S 9731
9.5%
B 8510
8.3%
A 7383
 
7.2%
T 6416
 
6.3%
E 5767
 
5.7%
C 4891
 
4.8%
I 4758
 
4.7%
O 4675
 
4.6%
Other values (6) 17989
17.6%
Lowercase Letter
ValueCountFrequency (%)
d 488
50.0%
o 488
50.0%
Space Separator
ValueCountFrequency (%)
976
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 102940
99.1%
Common 976
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 19503
18.9%
D 12341
12.0%
S 9731
9.5%
B 8510
8.3%
A 7383
 
7.2%
T 6416
 
6.2%
E 5767
 
5.6%
C 4891
 
4.8%
I 4758
 
4.6%
O 4675
 
4.5%
Other values (8) 18965
18.4%
Common
ValueCountFrequency (%)
976
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 19503
18.8%
D 12341
11.9%
S 9731
9.4%
B 8510
8.2%
A 7383
 
7.1%
T 6416
 
6.2%
E 5767
 
5.5%
C 4891
 
4.7%
I 4758
 
4.6%
O 4675
 
4.5%
Other values (9) 19941
19.2%

NM_PARTIDO
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
MOVIMENTO DEMOCRÁTICO BRASILEIRO
2234 
PARTIDO SOCIAL CRISTÃO
1700 
PARTIDO LIBERAL
1669 
PARTIDO SOCIAL DEMOCRÁTICO
1613 
PARTIDO DA SOCIAL DEMOCRACIA BRASILEIRA
 
1539
Other values (26)
15828 

Length

Max length46
Median length30
Mean length22.577065
Min length6

Characters and Unicode

Total characters555012
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPROGRESSISTAS
2nd rowPARTIDO DOS TRABALHADORES
3rd rowPARTIDO SOCIAL DEMOCRÁTICO
4th rowAVANTE
5th rowPARTIDO DEMOCRÁTICO TRABALHISTA

Common Values

ValueCountFrequency (%)
MOVIMENTO DEMOCRÁTICO BRASILEIRO 2234
 
9.1%
PARTIDO SOCIAL CRISTÃO 1700
 
6.9%
PARTIDO LIBERAL 1669
 
6.8%
PARTIDO SOCIAL DEMOCRÁTICO 1613
 
6.6%
PARTIDO DA SOCIAL DEMOCRACIA BRASILEIRA 1539
 
6.3%
PARTIDO DOS TRABALHADORES 1473
 
6.0%
REPUBLICANOS 1462
 
5.9%
DEMOCRATAS 1246
 
5.1%
PARTIDO DEMOCRÁTICO TRABALHISTA 1229
 
5.0%
PARTIDO SOCIALISTA BRASILEIRO 1171
 
4.8%
Other values (21) 9247
37.6%

Length

2023-07-12T17:34:11.235953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
partido 15066
23.7%
social 6137
 
9.6%
democrático 5076
 
8.0%
brasileiro 4894
 
7.7%
trabalhista 3038
 
4.8%
da 2555
 
4.0%
movimento 2234
 
3.5%
liberal 2192
 
3.4%
cristão 2020
 
3.2%
democracia 1725
 
2.7%
Other values (32) 18677
29.4%

Most occurring characters

ValueCountFrequency (%)
A 67867
12.2%
I 62277
11.2%
O 59516
10.7%
R 54916
9.9%
39031
 
7.0%
T 38102
 
6.9%
D 36230
 
6.5%
S 32500
 
5.9%
E 30319
 
5.5%
C 28306
 
5.1%
Other values (14) 105948
19.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 515981
93.0%
Space Separator 39031
 
7.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 67867
13.2%
I 62277
12.1%
O 59516
11.5%
R 54916
10.6%
T 38102
7.4%
D 36230
7.0%
S 32500
6.3%
E 30319
 
5.9%
C 28306
 
5.5%
L 27389
 
5.3%
Other values (13) 78559
15.2%
Space Separator
ValueCountFrequency (%)
39031
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 515981
93.0%
Common 39031
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 67867
13.2%
I 62277
12.1%
O 59516
11.5%
R 54916
10.6%
T 38102
7.4%
D 36230
7.0%
S 32500
6.3%
E 30319
 
5.9%
C 28306
 
5.5%
L 27389
 
5.3%
Other values (13) 78559
15.2%
Common
ValueCountFrequency (%)
39031
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 547476
98.6%
None 7536
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 67867
12.4%
I 62277
11.4%
O 59516
10.9%
R 54916
10.0%
39031
 
7.1%
T 38102
 
7.0%
D 36230
 
6.6%
S 32500
 
5.9%
E 30319
 
5.5%
C 28306
 
5.2%
Other values (11) 98412
18.0%
None
ValueCountFrequency (%)
Á 5076
67.4%
à 2333
31.0%
Ç 127
 
1.7%

NR_FEDERACAO
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
-1
24583 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters49166
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 24583
100.0%

Length

2023-07-12T17:34:11.527772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:11.757306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 24583
100.0%

Most occurring characters

ValueCountFrequency (%)
- 24583
50.0%
1 24583
50.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 24583
50.0%
Decimal Number 24583
50.0%

Most frequent character per category

Dash Punctuation
ValueCountFrequency (%)
- 24583
100.0%
Decimal Number
ValueCountFrequency (%)
1 24583
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49166
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 24583
50.0%
1 24583
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 24583
50.0%
1 24583
50.0%

NM_FEDERACAO
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
#NULO#
24583 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters147498
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row#NULO#
2nd row#NULO#
3rd row#NULO#
4th row#NULO#
5th row#NULO#

Common Values

ValueCountFrequency (%)
#NULO# 24583
100.0%

Length

2023-07-12T17:34:11.946189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:12.180045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nulo 24583
100.0%

Most occurring characters

ValueCountFrequency (%)
# 49166
33.3%
N 24583
16.7%
U 24583
16.7%
L 24583
16.7%
O 24583
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 98332
66.7%
Other Punctuation 49166
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 24583
25.0%
U 24583
25.0%
L 24583
25.0%
O 24583
25.0%
Other Punctuation
ValueCountFrequency (%)
# 49166
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 98332
66.7%
Common 49166
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 24583
25.0%
U 24583
25.0%
L 24583
25.0%
O 24583
25.0%
Common
ValueCountFrequency (%)
# 49166
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
# 49166
33.3%
N 24583
16.7%
U 24583
16.7%
L 24583
16.7%
O 24583
16.7%

SG_FEDERACAO
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
#NULO#
24583 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters147498
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row#NULO#
2nd row#NULO#
3rd row#NULO#
4th row#NULO#
5th row#NULO#

Common Values

ValueCountFrequency (%)
#NULO# 24583
100.0%

Length

2023-07-12T17:34:12.368930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:12.598789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nulo 24583
100.0%

Most occurring characters

ValueCountFrequency (%)
# 49166
33.3%
N 24583
16.7%
U 24583
16.7%
L 24583
16.7%
O 24583
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 98332
66.7%
Other Punctuation 49166
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 24583
25.0%
U 24583
25.0%
L 24583
25.0%
O 24583
25.0%
Other Punctuation
ValueCountFrequency (%)
# 49166
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 98332
66.7%
Common 49166
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 24583
25.0%
U 24583
25.0%
L 24583
25.0%
O 24583
25.0%
Common
ValueCountFrequency (%)
# 49166
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
# 49166
33.3%
N 24583
16.7%
U 24583
16.7%
L 24583
16.7%
O 24583
16.7%

DS_COMPOSICAO_FEDERACAO
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
#NULO#
24583 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters147498
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row#NULO#
2nd row#NULO#
3rd row#NULO#
4th row#NULO#
5th row#NULO#

Common Values

ValueCountFrequency (%)
#NULO# 24583
100.0%

Length

2023-07-12T17:34:12.795667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:13.026972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nulo 24583
100.0%

Most occurring characters

ValueCountFrequency (%)
# 49166
33.3%
N 24583
16.7%
U 24583
16.7%
L 24583
16.7%
O 24583
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 98332
66.7%
Other Punctuation 49166
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 24583
25.0%
U 24583
25.0%
L 24583
25.0%
O 24583
25.0%
Other Punctuation
ValueCountFrequency (%)
# 49166
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 98332
66.7%
Common 49166
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 24583
25.0%
U 24583
25.0%
L 24583
25.0%
O 24583
25.0%
Common
ValueCountFrequency (%)
# 49166
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
# 49166
33.3%
N 24583
16.7%
U 24583
16.7%
L 24583
16.7%
O 24583
16.7%

SQ_COLIGACAO
Real number (ℝ)

HIGH CORRELATION 

Distinct1788
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2969502 × 1011
Minimum3.0000052 × 1010
Maximum1.4000019 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.1 KiB
2023-07-12T17:34:13.497684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.0000052 × 1010
5-th percentile3.0000099 × 1010
Q11.4000007 × 1011
median1.4000011 × 1011
Q31.4000013 × 1011
95-th percentile1.4000015 × 1011
Maximum1.4000019 × 1011
Range1.1000014 × 1011
Interquartile range (IQR)62660

Descriptive statistics

Standard deviation3.2053188 × 1010
Coefficient of variation (CV)0.24714278
Kurtosis5.7791235
Mean1.2969502 × 1011
Median Absolute Deviation (MAD)30753
Skewness-2.7890237
Sum3.1882927 × 1015
Variance1.0274069 × 1021
MonotonicityNot monotonic
2023-07-12T17:34:13.808494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.400000603 × 101155
 
0.2%
1.400001383 × 101154
 
0.2%
1.400001084 × 101154
 
0.2%
1.400001553 × 101153
 
0.2%
1.400000734 × 101153
 
0.2%
1.400000734 × 101153
 
0.2%
1.400000888 × 101148
 
0.2%
1.400001473 × 101147
 
0.2%
1.400001295 × 101147
 
0.2%
1.400000714 × 101147
 
0.2%
Other values (1778) 24072
97.9%
ValueCountFrequency (%)
3.000005169 × 101010
 
< 0.1%
3.000005185 × 101025
0.1%
3.000005344 × 101011
< 0.1%
3.000005347 × 101011
< 0.1%
3.000005347 × 101010
 
< 0.1%
3.000005365 × 101010
 
< 0.1%
3.000005574 × 101010
 
< 0.1%
3.000005592 × 101013
0.1%
3.000005595 × 101013
0.1%
3.000005613 × 101011
< 0.1%
ValueCountFrequency (%)
1.400001886 × 101110
< 0.1%
1.400001749 × 10114
 
< 0.1%
1.400001743 × 10111
 
< 0.1%
1.400001702 × 10112
 
< 0.1%
1.400001677 × 10115
 
< 0.1%
1.400001647 × 101111
< 0.1%
1.400001634 × 101116
0.1%
1.400001631 × 101116
0.1%
1.400001627 × 10116
 
< 0.1%
1.400001627 × 101118
0.1%

NM_COLIGACAO
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
PARTIDO ISOLADO
24583 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters368745
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARTIDO ISOLADO
2nd rowPARTIDO ISOLADO
3rd rowPARTIDO ISOLADO
4th rowPARTIDO ISOLADO
5th rowPARTIDO ISOLADO

Common Values

ValueCountFrequency (%)
PARTIDO ISOLADO 24583
100.0%

Length

2023-07-12T17:34:14.091317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:14.323172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
partido 24583
50.0%
isolado 24583
50.0%

Most occurring characters

ValueCountFrequency (%)
O 73749
20.0%
A 49166
13.3%
I 49166
13.3%
D 49166
13.3%
P 24583
 
6.7%
R 24583
 
6.7%
T 24583
 
6.7%
24583
 
6.7%
S 24583
 
6.7%
L 24583
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 344162
93.3%
Space Separator 24583
 
6.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 73749
21.4%
A 49166
14.3%
I 49166
14.3%
D 49166
14.3%
P 24583
 
7.1%
R 24583
 
7.1%
T 24583
 
7.1%
S 24583
 
7.1%
L 24583
 
7.1%
Space Separator
ValueCountFrequency (%)
24583
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 344162
93.3%
Common 24583
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 73749
21.4%
A 49166
14.3%
I 49166
14.3%
D 49166
14.3%
P 24583
 
7.1%
R 24583
 
7.1%
T 24583
 
7.1%
S 24583
 
7.1%
L 24583
 
7.1%
Common
ValueCountFrequency (%)
24583
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 368745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 73749
20.0%
A 49166
13.3%
I 49166
13.3%
D 49166
13.3%
P 24583
 
6.7%
R 24583
 
6.7%
T 24583
 
6.7%
24583
 
6.7%
S 24583
 
6.7%
L 24583
 
6.7%

DS_COMPOSICAO_COLIGACAO
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
MDB
2234 
PSC
1700 
PL
1669 
PSD
1613 
PSDB
 
1539
Other values (26)
15828 

Length

Max length13
Median length12
Mean length4.2271488
Min length2

Characters and Unicode

Total characters103916
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPP
2nd rowPT
3rd rowPSD
4th rowAVANTE
5th rowPDT

Common Values

ValueCountFrequency (%)
MDB 2234
 
9.1%
PSC 1700
 
6.9%
PL 1669
 
6.8%
PSD 1613
 
6.6%
PSDB 1539
 
6.3%
PT 1473
 
6.0%
REPUBLICANOS 1462
 
5.9%
DEM 1246
 
5.1%
PDT 1229
 
5.0%
PSB 1171
 
4.8%
Other values (21) 9247
37.6%

Length

2023-07-12T17:34:14.537042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mdb 2234
 
8.7%
psc 1700
 
6.7%
pl 1669
 
6.5%
psd 1613
 
6.3%
psdb 1539
 
6.0%
pt 1473
 
5.8%
republicanos 1462
 
5.7%
dem 1246
 
4.9%
pdt 1229
 
4.8%
psb 1171
 
4.6%
Other values (23) 10223
40.0%

Most occurring characters

ValueCountFrequency (%)
P 19503
18.8%
D 12341
11.9%
S 9731
9.4%
B 8510
8.2%
A 7383
 
7.1%
T 6416
 
6.2%
E 5767
 
5.5%
C 4891
 
4.7%
I 4758
 
4.6%
O 4675
 
4.5%
Other values (9) 19941
19.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 101964
98.1%
Space Separator 976
 
0.9%
Lowercase Letter 976
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 19503
19.1%
D 12341
12.1%
S 9731
9.5%
B 8510
8.3%
A 7383
 
7.2%
T 6416
 
6.3%
E 5767
 
5.7%
C 4891
 
4.8%
I 4758
 
4.7%
O 4675
 
4.6%
Other values (6) 17989
17.6%
Lowercase Letter
ValueCountFrequency (%)
d 488
50.0%
o 488
50.0%
Space Separator
ValueCountFrequency (%)
976
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 102940
99.1%
Common 976
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 19503
18.9%
D 12341
12.0%
S 9731
9.5%
B 8510
8.3%
A 7383
 
7.2%
T 6416
 
6.2%
E 5767
 
5.6%
C 4891
 
4.8%
I 4758
 
4.6%
O 4675
 
4.5%
Other values (8) 18965
18.4%
Common
ValueCountFrequency (%)
976
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 19503
18.8%
D 12341
11.9%
S 9731
9.4%
B 8510
8.2%
A 7383
 
7.1%
T 6416
 
6.2%
E 5767
 
5.5%
C 4891
 
4.7%
I 4758
 
4.6%
O 4675
 
4.5%
Other values (9) 19941
19.2%

CD_NACIONALIDADE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
1
24457 
2
 
122
3
 
3
-4
 
1

Length

Max length2
Median length1
Mean length1.0000407
Min length1

Characters and Unicode

Total characters24584
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 24457
99.5%
2 122
 
0.5%
3 3
 
< 0.1%
-4 1
 
< 0.1%

Length

2023-07-12T17:34:14.804878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:15.062718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 24457
99.5%
2 122
 
0.5%
3 3
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 24457
99.5%
2 122
 
0.5%
3 3
 
< 0.1%
- 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24583
> 99.9%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 24457
99.5%
2 122
 
0.5%
3 3
 
< 0.1%
4 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24584
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 24457
99.5%
2 122
 
0.5%
3 3
 
< 0.1%
- 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 24457
99.5%
2 122
 
0.5%
3 3
 
< 0.1%
- 1
 
< 0.1%
4 1
 
< 0.1%

DS_NACIONALIDADE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
BRASILEIRA NATA
24457 
BRASILEIRA (NATURALIZADA)
 
122
PORTUGUESA COM IGUALDADE DE DIREITOS
 
3
NÃO DIVULGÁVEL
 
1

Length

Max length36
Median length15
Mean length15.05215
Min length14

Characters and Unicode

Total characters370027
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBRASILEIRA NATA
2nd rowBRASILEIRA NATA
3rd rowBRASILEIRA NATA
4th rowBRASILEIRA NATA
5th rowBRASILEIRA NATA

Common Values

ValueCountFrequency (%)
BRASILEIRA NATA 24457
99.5%
BRASILEIRA (NATURALIZADA) 122
 
0.5%
PORTUGUESA COM IGUALDADE DE DIREITOS 3
 
< 0.1%
NÃO DIVULGÁVEL 1
 
< 0.1%

Length

2023-07-12T17:34:15.278259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:15.531108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
brasileira 24579
50.0%
nata 24457
49.7%
naturalizada 122
 
0.2%
portuguesa 3
 
< 0.1%
com 3
 
< 0.1%
igualdade 3
 
< 0.1%
de 3
 
< 0.1%
direitos 3
 
< 0.1%
não 1
 
< 0.1%
divulgável 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 98569
26.6%
I 49290
13.3%
R 49286
13.3%
L 24706
 
6.7%
E 24592
 
6.6%
24592
 
6.6%
S 24585
 
6.6%
T 24585
 
6.6%
N 24580
 
6.6%
B 24579
 
6.6%
Other values (13) 663
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 345191
93.3%
Space Separator 24592
 
6.6%
Open Punctuation 122
 
< 0.1%
Close Punctuation 122
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 98569
28.6%
I 49290
14.3%
R 49286
14.3%
L 24706
 
7.2%
E 24592
 
7.1%
S 24585
 
7.1%
T 24585
 
7.1%
N 24580
 
7.1%
B 24579
 
7.1%
D 135
 
< 0.1%
Other values (10) 284
 
0.1%
Space Separator
ValueCountFrequency (%)
24592
100.0%
Open Punctuation
ValueCountFrequency (%)
( 122
100.0%
Close Punctuation
ValueCountFrequency (%)
) 122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 345191
93.3%
Common 24836
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 98569
28.6%
I 49290
14.3%
R 49286
14.3%
L 24706
 
7.2%
E 24592
 
7.1%
S 24585
 
7.1%
T 24585
 
7.1%
N 24580
 
7.1%
B 24579
 
7.1%
D 135
 
< 0.1%
Other values (10) 284
 
0.1%
Common
ValueCountFrequency (%)
24592
99.0%
( 122
 
0.5%
) 122
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 370025
> 99.9%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 98569
26.6%
I 49290
13.3%
R 49286
13.3%
L 24706
 
6.7%
E 24592
 
6.6%
24592
 
6.6%
S 24585
 
6.6%
T 24585
 
6.6%
N 24580
 
6.6%
B 24579
 
6.6%
Other values (11) 661
 
0.2%
None
ValueCountFrequency (%)
à 1
50.0%
Á 1
50.0%

SG_UF_NASCIMENTO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
PA
17937 
MA
1920 
AP
 
1541
TO
 
603
GO
 
426
Other values (24)
2156 

Length

Max length14
Median length2
Mean length2.0004881
Min length2

Characters and Unicode

Total characters49178
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAP
2nd rowPA
3rd rowMA
4th rowAP
5th rowAP

Common Values

ValueCountFrequency (%)
PA 17937
73.0%
MA 1920
 
7.8%
AP 1541
 
6.3%
TO 603
 
2.5%
GO 426
 
1.7%
CE 370
 
1.5%
BA 269
 
1.1%
MG 237
 
1.0%
PI 227
 
0.9%
PR 159
 
0.6%
Other values (19) 894
 
3.6%

Length

2023-07-12T17:34:15.790943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pa 17937
73.0%
ma 1920
 
7.8%
ap 1541
 
6.3%
to 603
 
2.5%
go 426
 
1.7%
ce 370
 
1.5%
ba 269
 
1.1%
mg 237
 
1.0%
pi 227
 
0.9%
pr 159
 
0.6%
Other values (20) 895
 
3.6%

Most occurring characters

ValueCountFrequency (%)
A 21831
44.4%
P 20109
40.9%
M 2371
 
4.8%
O 1056
 
2.1%
G 663
 
1.3%
T 662
 
1.3%
E 621
 
1.3%
C 414
 
0.8%
S 357
 
0.7%
R 345
 
0.7%
Other values (19) 749
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 49165
> 99.9%
Lowercase Letter 12
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 21831
44.4%
P 20109
40.9%
M 2371
 
4.8%
O 1056
 
2.1%
G 663
 
1.3%
T 662
 
1.3%
E 621
 
1.3%
C 414
 
0.8%
S 357
 
0.7%
R 345
 
0.7%
Other values (8) 736
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
v 2
16.7%
l 2
16.7%
ã 1
8.3%
o 1
8.3%
d 1
8.3%
i 1
8.3%
u 1
8.3%
g 1
8.3%
á 1
8.3%
e 1
8.3%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49177
> 99.9%
Common 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 21831
44.4%
P 20109
40.9%
M 2371
 
4.8%
O 1056
 
2.1%
G 663
 
1.3%
T 662
 
1.3%
E 621
 
1.3%
C 414
 
0.8%
S 357
 
0.7%
R 345
 
0.7%
Other values (18) 748
 
1.5%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49176
> 99.9%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 21831
44.4%
P 20109
40.9%
M 2371
 
4.8%
O 1056
 
2.1%
G 663
 
1.3%
T 662
 
1.3%
E 621
 
1.3%
C 414
 
0.8%
S 357
 
0.7%
R 345
 
0.7%
Other values (17) 747
 
1.5%
None
ValueCountFrequency (%)
ã 1
50.0%
á 1
50.0%

CD_MUNICIPIO_NASCIMENTO
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
-3
24583 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters49166
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-3
2nd row-3
3rd row-3
4th row-3
5th row-3

Common Values

ValueCountFrequency (%)
-3 24583
100.0%

Length

2023-07-12T17:34:16.062442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:16.296295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 24583
100.0%

Most occurring characters

ValueCountFrequency (%)
- 24583
50.0%
3 24583
50.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 24583
50.0%
Decimal Number 24583
50.0%

Most frequent character per category

Dash Punctuation
ValueCountFrequency (%)
- 24583
100.0%
Decimal Number
ValueCountFrequency (%)
3 24583
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49166
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 24583
50.0%
3 24583
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 24583
50.0%
3 24583
50.0%
Distinct1320
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
2023-07-12T17:34:16.750019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length28
Median length24
Mean length9.3999919
Min length3

Characters and Unicode

Total characters231080
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique570 ?
Unique (%)2.3%

Sample

1st rowAMAPÁ
2nd rowBELÉM
3rd rowPIO XII
4th rowSERRA DO NAVIO
5th rowAMAPÁ
ValueCountFrequency (%)
belém 2831
 
7.8%
do 2805
 
7.7%
são 1330
 
3.7%
macapá 792
 
2.2%
santarém 769
 
2.1%
pará 692
 
1.9%
santa 617
 
1.7%
de 597
 
1.6%
araguaia 541
 
1.5%
castanhal 464
 
1.3%
Other values (1275) 24933
68.6%
2023-07-12T17:34:17.686440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 37834
16.4%
R 16903
 
7.3%
O 16234
 
7.0%
I 15416
 
6.7%
E 14509
 
6.3%
M 11939
 
5.2%
11788
 
5.1%
N 11289
 
4.9%
T 9993
 
4.3%
U 9699
 
4.2%
Other values (29) 75476
32.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 218630
94.6%
Space Separator 11788
 
5.1%
Dash Punctuation 662
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 37834
17.3%
R 16903
 
7.7%
O 16234
 
7.4%
I 15416
 
7.1%
E 14509
 
6.6%
M 11939
 
5.5%
N 11289
 
5.2%
T 9993
 
4.6%
U 9699
 
4.4%
S 9696
 
4.4%
Other values (27) 65118
29.8%
Space Separator
ValueCountFrequency (%)
11788
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 662
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 218630
94.6%
Common 12450
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 37834
17.3%
R 16903
 
7.7%
O 16234
 
7.4%
I 15416
 
7.1%
E 14509
 
6.6%
M 11939
 
5.5%
N 11289
 
5.2%
T 9993
 
4.6%
U 9699
 
4.4%
S 9696
 
4.4%
Other values (27) 65118
29.8%
Common
ValueCountFrequency (%)
11788
94.7%
- 662
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 215092
93.1%
None 15988
 
6.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 37834
17.6%
R 16903
 
7.9%
O 16234
 
7.5%
I 15416
 
7.2%
E 14509
 
6.7%
M 11939
 
5.6%
11788
 
5.5%
N 11289
 
5.2%
T 9993
 
4.6%
U 9699
 
4.5%
Other values (18) 59488
27.7%
None
ValueCountFrequency (%)
É 4654
29.1%
Á 4298
26.9%
à 3072
19.2%
Ç 1817
 
11.4%
Ó 757
 
4.7%
Í 645
 
4.0%
 303
 
1.9%
Ê 205
 
1.3%
Ô 142
 
0.9%
Ú 91
 
0.6%
Distinct12302
Distinct (%)50.0%
Missing1
Missing (%)< 0.1%
Memory size384.1 KiB
Minimum1929-04-23 00:00:00
Maximum2003-06-30 00:00:00
2023-07-12T17:34:18.027235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:34:18.355027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NR_IDADE_DATA_POSSE
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean43.164185
Minimum17
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.1 KiB
2023-07-12T17:34:18.653846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q136
median43
Q350
95-th percentile62
Maximum91
Range74
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.873541
Coefficient of variation (CV)0.25191118
Kurtosis-0.16993216
Mean43.164185
Median Absolute Deviation (MAD)7
Skewness0.23723747
Sum1061062
Variance118.23389
MonotonicityNot monotonic
2023-07-12T17:34:18.977644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 935
 
3.8%
41 905
 
3.7%
42 904
 
3.7%
39 903
 
3.7%
38 900
 
3.7%
43 876
 
3.6%
46 868
 
3.5%
44 856
 
3.5%
37 852
 
3.5%
45 826
 
3.4%
Other values (60) 15757
64.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
18 37
 
0.2%
19 70
 
0.3%
20 91
 
0.4%
21 150
0.6%
22 181
0.7%
23 207
0.8%
24 195
0.8%
25 260
1.1%
26 325
1.3%
ValueCountFrequency (%)
91 1
 
< 0.1%
88 2
 
< 0.1%
85 1
 
< 0.1%
83 1
 
< 0.1%
82 3
 
< 0.1%
81 3
 
< 0.1%
80 5
< 0.1%
79 6
< 0.1%
78 7
< 0.1%
77 9
< 0.1%

NR_TITULO_ELEITORAL_CANDIDATO
Real number (ℝ)

HIGH CORRELATION 

Distinct24555
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4243392 × 1010
Minimum-4
Maximum4.0925039 × 1011
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size384.1 KiB
2023-07-12T17:34:19.323430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile2.4405606 × 109
Q11.8890951 × 1010
median3.3094161 × 1010
Q34.7447761 × 1010
95-th percentile6.8993506 × 1010
Maximum4.0925039 × 1011
Range4.0925039 × 1011
Interquartile range (IQR)2.855681 × 1010

Descriptive statistics

Standard deviation2.4222232 × 1010
Coefficient of variation (CV)0.70735494
Kurtosis29.939126
Mean3.4243392 × 1010
Median Absolute Deviation (MAD)1.427429 × 1010
Skewness3.0175436
Sum8.418053 × 1014
Variance5.8671653 × 1020
MonotonicityNot monotonic
2023-07-12T17:34:19.622246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2780022500 2
 
< 0.1%
2.370535133 × 10102
 
< 0.1%
3.277062137 × 10102
 
< 0.1%
3.111942135 × 10102
 
< 0.1%
7.933903134 × 10102
 
< 0.1%
4.603794132 × 10102
 
< 0.1%
5.206662132 × 10102
 
< 0.1%
4.280375137 × 10102
 
< 0.1%
3.423386135 × 10102
 
< 0.1%
3.390338138 × 10102
 
< 0.1%
Other values (24545) 24563
99.9%
ValueCountFrequency (%)
-4 1
< 0.1%
4252518 1
< 0.1%
6792534 1
< 0.1%
7851341 1
< 0.1%
9122216 1
< 0.1%
14082577 1
< 0.1%
15322569 1
< 0.1%
17332577 1
< 0.1%
17462593 1
< 0.1%
21182500 1
< 0.1%
ValueCountFrequency (%)
4.092503901 × 10111
< 0.1%
3.999299602 × 10111
< 0.1%
3.936187801 × 10111
< 0.1%
3.872760602 × 10111
< 0.1%
3.766805902 × 10111
< 0.1%
3.683882001 × 10111
< 0.1%
3.619443402 × 10111
< 0.1%
3.486719802 × 10111
< 0.1%
3.275816001 × 10111
< 0.1%
3.127973001 × 10111
< 0.1%

CD_GENERO
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
2
16007 
4
8575 
-4
 
1

Length

Max length2
Median length1
Mean length1.0000407
Min length1

Characters and Unicode

Total characters24584
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 16007
65.1%
4 8575
34.9%
-4 1
 
< 0.1%

Length

2023-07-12T17:34:19.895444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:20.152285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 16007
65.1%
4 8576
34.9%

Most occurring characters

ValueCountFrequency (%)
2 16007
65.1%
4 8576
34.9%
- 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24583
> 99.9%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 16007
65.1%
4 8576
34.9%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24584
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 16007
65.1%
4 8576
34.9%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 16007
65.1%
4 8576
34.9%
- 1
 
< 0.1%

DS_GENERO
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
MASCULINO
16007 
FEMININO
8575 
NÃO DIVULGÁVEL
 
1

Length

Max length14
Median length9
Mean length8.6513851
Min length8

Characters and Unicode

Total characters212677
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMASCULINO
2nd rowMASCULINO
3rd rowMASCULINO
4th rowMASCULINO
5th rowMASCULINO

Common Values

ValueCountFrequency (%)
MASCULINO 16007
65.1%
FEMININO 8575
34.9%
NÃO DIVULGÁVEL 1
 
< 0.1%

Length

2023-07-12T17:34:20.387303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:20.659419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
masculino 16007
65.1%
feminino 8575
34.9%
não 1
 
< 0.1%
divulgável 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I 33158
15.6%
N 33158
15.6%
O 24583
11.6%
M 24582
11.6%
L 16009
7.5%
U 16008
7.5%
C 16007
7.5%
S 16007
7.5%
A 16007
7.5%
E 8576
 
4.0%
Other values (7) 8582
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 212676
> 99.9%
Space Separator 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 33158
15.6%
N 33158
15.6%
O 24583
11.6%
M 24582
11.6%
L 16009
7.5%
U 16008
7.5%
C 16007
7.5%
S 16007
7.5%
A 16007
7.5%
E 8576
 
4.0%
Other values (6) 8581
 
4.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 212676
> 99.9%
Common 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 33158
15.6%
N 33158
15.6%
O 24583
11.6%
M 24582
11.6%
L 16009
7.5%
U 16008
7.5%
C 16007
7.5%
S 16007
7.5%
A 16007
7.5%
E 8576
 
4.0%
Other values (6) 8581
 
4.0%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 212675
> 99.9%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 33158
15.6%
N 33158
15.6%
O 24583
11.6%
M 24582
11.6%
L 16009
7.5%
U 16008
7.5%
C 16007
7.5%
S 16007
7.5%
A 16007
7.5%
E 8576
 
4.0%
Other values (5) 8580
 
4.0%
None
ValueCountFrequency (%)
à 1
50.0%
Á 1
50.0%

CD_GRAU_INSTRUCAO
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5653907
Minimum-4
Maximum8
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size384.1 KiB
2023-07-12T17:34:20.851301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile3
Q14
median6
Q36
95-th percentile8
Maximum8
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8011856
Coefficient of variation (CV)0.32364046
Kurtosis-0.89176211
Mean5.5653907
Median Absolute Deviation (MAD)2
Skewness-0.2807194
Sum136814
Variance3.2442696
MonotonicityNot monotonic
2023-07-12T17:34:21.089151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
6 9579
39.0%
8 5350
21.8%
4 3311
 
13.5%
3 3300
 
13.4%
5 1131
 
4.6%
2 1124
 
4.6%
7 785
 
3.2%
1 2
 
< 0.1%
-4 1
 
< 0.1%
ValueCountFrequency (%)
-4 1
 
< 0.1%
1 2
 
< 0.1%
2 1124
 
4.6%
3 3300
 
13.4%
4 3311
 
13.5%
5 1131
 
4.6%
6 9579
39.0%
7 785
 
3.2%
8 5350
21.8%
ValueCountFrequency (%)
8 5350
21.8%
7 785
 
3.2%
6 9579
39.0%
5 1131
 
4.6%
4 3311
 
13.5%
3 3300
 
13.4%
2 1124
 
4.6%
1 2
 
< 0.1%
-4 1
 
< 0.1%

DS_GRAU_INSTRUCAO
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
ENSINO MÉDIO COMPLETO
9579 
SUPERIOR COMPLETO
5350 
ENSINO FUNDAMENTAL COMPLETO
3311 
ENSINO FUNDAMENTAL INCOMPLETO
3300 
ENSINO MÉDIO INCOMPLETO
1131 
Other values (4)
1912 

Length

Max length29
Median length27
Mean length21.626978
Min length10

Characters and Unicode

Total characters531656
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowENSINO MÉDIO COMPLETO
2nd rowENSINO MÉDIO COMPLETO
3rd rowENSINO MÉDIO COMPLETO
4th rowSUPERIOR COMPLETO
5th rowENSINO MÉDIO COMPLETO

Common Values

ValueCountFrequency (%)
ENSINO MÉDIO COMPLETO 9579
39.0%
SUPERIOR COMPLETO 5350
21.8%
ENSINO FUNDAMENTAL COMPLETO 3311
 
13.5%
ENSINO FUNDAMENTAL INCOMPLETO 3300
 
13.4%
ENSINO MÉDIO INCOMPLETO 1131
 
4.6%
LÊ E ESCREVE 1124
 
4.6%
SUPERIOR INCOMPLETO 785
 
3.2%
ANALFABETO 2
 
< 0.1%
NÃO DIVULGÁVEL 1
 
< 0.1%

Length

2023-07-12T17:34:21.370980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:21.706429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
completo 18240
27.0%
ensino 17321
25.6%
médio 10710
15.8%
fundamental 6611
 
9.8%
superior 6135
 
9.1%
incompleto 5216
 
7.7%
1124
 
1.7%
e 1124
 
1.7%
escreve 1124
 
1.7%
analfabeto 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 81081
15.3%
E 58022
10.9%
N 53083
10.0%
43026
8.1%
M 40777
7.7%
I 39383
 
7.4%
L 31195
 
5.9%
T 30069
 
5.7%
P 29591
 
5.6%
C 24580
 
4.6%
Other values (13) 100849
19.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 488630
91.9%
Space Separator 43026
 
8.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 81081
16.6%
E 58022
11.9%
N 53083
10.9%
M 40777
8.3%
I 39383
8.1%
L 31195
 
6.4%
T 30069
 
6.2%
P 29591
 
6.1%
C 24580
 
5.0%
S 24580
 
5.0%
Other values (12) 76269
15.6%
Space Separator
ValueCountFrequency (%)
43026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 488630
91.9%
Common 43026
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 81081
16.6%
E 58022
11.9%
N 53083
10.9%
M 40777
8.3%
I 39383
8.1%
L 31195
 
6.4%
T 30069
 
6.2%
P 29591
 
6.1%
C 24580
 
5.0%
S 24580
 
5.0%
Other values (12) 76269
15.6%
Common
ValueCountFrequency (%)
43026
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 519820
97.8%
None 11836
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 81081
15.6%
E 58022
11.2%
N 53083
10.2%
43026
8.3%
M 40777
7.8%
I 39383
7.6%
L 31195
 
6.0%
T 30069
 
5.8%
P 29591
 
5.7%
C 24580
 
4.7%
Other values (9) 89013
17.1%
None
ValueCountFrequency (%)
É 10710
90.5%
Ê 1124
 
9.5%
à 1
 
< 0.1%
Á 1
 
< 0.1%

CD_ESTADO_CIVIL
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2249522
Minimum-4
Maximum9
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size384.1 KiB
2023-07-12T17:34:21.987257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile1
Q11
median1
Q33
95-th percentile7
Maximum9
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8372254
Coefficient of variation (CV)0.82573702
Kurtosis6.0117702
Mean2.2249522
Median Absolute Deviation (MAD)0
Skewness2.3162213
Sum54696
Variance3.3753972
MonotonicityNot monotonic
2023-07-12T17:34:22.218113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 13434
54.6%
3 9623
39.1%
9 1153
 
4.7%
5 292
 
1.2%
7 80
 
0.3%
-4 1
 
< 0.1%
ValueCountFrequency (%)
-4 1
 
< 0.1%
1 13434
54.6%
3 9623
39.1%
5 292
 
1.2%
7 80
 
0.3%
9 1153
 
4.7%
ValueCountFrequency (%)
9 1153
 
4.7%
7 80
 
0.3%
5 292
 
1.2%
3 9623
39.1%
1 13434
54.6%
-4 1
 
< 0.1%

DS_ESTADO_CIVIL
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
SOLTEIRO(A)
13434 
CASADO(A)
9623 
DIVORCIADO(A)
 
1153
VIÚVO(A)
 
292
SEPARADO(A) JUDICIALMENTE
 
80

Length

Max length25
Median length11
Mean length10.320954
Min length8

Characters and Unicode

Total characters253720
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSOLTEIRO(A)
2nd rowCASADO(A)
3rd rowSOLTEIRO(A)
4th rowSOLTEIRO(A)
5th rowSOLTEIRO(A)

Common Values

ValueCountFrequency (%)
SOLTEIRO(A) 13434
54.6%
CASADO(A) 9623
39.1%
DIVORCIADO(A) 1153
 
4.7%
VIÚVO(A) 292
 
1.2%
SEPARADO(A) JUDICIALMENTE 80
 
0.3%
NÃO DIVULGÁVEL 1
 
< 0.1%

Length

2023-07-12T17:34:22.499939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:22.815743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
solteiro(a 13434
54.5%
casado(a 9623
39.0%
divorciado(a 1153
 
4.7%
viúvo(a 292
 
1.2%
separado(a 80
 
0.3%
judicialmente 80
 
0.3%
não 1
 
< 0.1%
divulgável 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 45221
17.8%
O 39170
15.4%
( 24582
9.7%
) 24582
9.7%
S 23137
9.1%
I 16193
 
6.4%
R 14667
 
5.8%
E 13675
 
5.4%
L 13516
 
5.3%
T 13514
 
5.3%
Other values (13) 25463
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 204475
80.6%
Open Punctuation 24582
 
9.7%
Close Punctuation 24582
 
9.7%
Space Separator 81
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 45221
22.1%
O 39170
19.2%
S 23137
11.3%
I 16193
 
7.9%
R 14667
 
7.2%
E 13675
 
6.7%
L 13516
 
6.6%
T 13514
 
6.6%
D 12090
 
5.9%
C 10856
 
5.3%
Other values (10) 2436
 
1.2%
Open Punctuation
ValueCountFrequency (%)
( 24582
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24582
100.0%
Space Separator
ValueCountFrequency (%)
81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 204475
80.6%
Common 49245
 
19.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 45221
22.1%
O 39170
19.2%
S 23137
11.3%
I 16193
 
7.9%
R 14667
 
7.2%
E 13675
 
6.7%
L 13516
 
6.6%
T 13514
 
6.6%
D 12090
 
5.9%
C 10856
 
5.3%
Other values (10) 2436
 
1.2%
Common
ValueCountFrequency (%)
( 24582
49.9%
) 24582
49.9%
81
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 253426
99.9%
None 294
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 45221
17.8%
O 39170
15.5%
( 24582
9.7%
) 24582
9.7%
S 23137
9.1%
I 16193
 
6.4%
R 14667
 
5.8%
E 13675
 
5.4%
L 13516
 
5.3%
T 13514
 
5.3%
Other values (10) 25169
9.9%
None
ValueCountFrequency (%)
Ú 292
99.3%
à 1
 
0.3%
Á 1
 
0.3%

CD_COR_RACA
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5960216
Minimum-4
Maximum6
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size384.1 KiB
2023-07-12T17:34:23.056605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile1
Q12
median3
Q33
95-th percentile3
Maximum6
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88852901
Coefficient of variation (CV)0.34226564
Kurtosis2.2303738
Mean2.5960216
Median Absolute Deviation (MAD)0
Skewness-0.10614216
Sum63818
Variance0.78948381
MonotonicityNot monotonic
2023-07-12T17:34:23.276462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 17074
69.5%
1 4261
 
17.3%
2 2725
 
11.1%
6 338
 
1.4%
5 125
 
0.5%
4 59
 
0.2%
-4 1
 
< 0.1%
ValueCountFrequency (%)
-4 1
 
< 0.1%
1 4261
 
17.3%
2 2725
 
11.1%
3 17074
69.5%
4 59
 
0.2%
5 125
 
0.5%
6 338
 
1.4%
ValueCountFrequency (%)
6 338
 
1.4%
5 125
 
0.5%
4 59
 
0.2%
3 17074
69.5%
2 2725
 
11.1%
1 4261
 
17.3%
-4 1
 
< 0.1%

DS_COR_RACA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
PARDA
17074 
BRANCA
4261 
PRETA
2725 
NÃO INFORMADO
 
338
INDÍGENA
 
125
Other values (2)
 
60

Length

Max length14
Median length5
Mean length5.3037465
Min length5

Characters and Unicode

Total characters130382
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPRETA
2nd rowPARDA
3rd rowPRETA
4th rowBRANCA
5th rowPARDA

Common Values

ValueCountFrequency (%)
PARDA 17074
69.5%
BRANCA 4261
 
17.3%
PRETA 2725
 
11.1%
NÃO INFORMADO 338
 
1.4%
INDÍGENA 125
 
0.5%
AMARELA 59
 
0.2%
NÃO DIVULGÁVEL 1
 
< 0.1%

Length

2023-07-12T17:34:23.520308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:23.805133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
parda 17074
68.5%
branca 4261
 
17.1%
preta 2725
 
10.9%
não 339
 
1.4%
informado 338
 
1.4%
indígena 125
 
0.5%
amarela 59
 
0.2%
divulgável 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 46035
35.3%
R 24457
18.8%
P 19799
15.2%
D 17538
 
13.5%
N 5188
 
4.0%
B 4261
 
3.3%
C 4261
 
3.3%
E 2910
 
2.2%
T 2725
 
2.1%
O 1015
 
0.8%
Other values (11) 2193
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 130043
99.7%
Space Separator 339
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 46035
35.4%
R 24457
18.8%
P 19799
15.2%
D 17538
 
13.5%
N 5188
 
4.0%
B 4261
 
3.3%
C 4261
 
3.3%
E 2910
 
2.2%
T 2725
 
2.1%
O 1015
 
0.8%
Other values (10) 1854
 
1.4%
Space Separator
ValueCountFrequency (%)
339
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 130043
99.7%
Common 339
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 46035
35.4%
R 24457
18.8%
P 19799
15.2%
D 17538
 
13.5%
N 5188
 
4.0%
B 4261
 
3.3%
C 4261
 
3.3%
E 2910
 
2.2%
T 2725
 
2.1%
O 1015
 
0.8%
Other values (10) 1854
 
1.4%
Common
ValueCountFrequency (%)
339
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129917
99.6%
None 465
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 46035
35.4%
R 24457
18.8%
P 19799
15.2%
D 17538
 
13.5%
N 5188
 
4.0%
B 4261
 
3.3%
C 4261
 
3.3%
E 2910
 
2.2%
T 2725
 
2.1%
O 1015
 
0.8%
Other values (8) 1728
 
1.3%
None
ValueCountFrequency (%)
à 339
72.9%
Í 125
 
26.9%
Á 1
 
0.2%

CD_OCUPACAO
Real number (ℝ)

HIGH CORRELATION 

Distinct194
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean552.46138
Minimum-4
Maximum999
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size384.1 KiB
2023-07-12T17:34:24.118942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile131
Q1265
median541
Q3999
95-th percentile999
Maximum999
Range1003
Interquartile range (IQR)734

Descriptive statistics

Standard deviation326.04692
Coefficient of variation (CV)0.59017143
Kurtosis-1.4775214
Mean552.46138
Median Absolute Deviation (MAD)287
Skewness0.28277549
Sum13581158
Variance106306.6
MonotonicityNot monotonic
2023-07-12T17:34:24.439742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999 6628
27.0%
601 2447
 
10.0%
298 1405
 
5.7%
278 1132
 
4.6%
169 1090
 
4.4%
265 1028
 
4.2%
257 858
 
3.5%
581 842
 
3.4%
604 681
 
2.8%
931 482
 
2.0%
Other values (184) 7990
32.5%
ValueCountFrequency (%)
-4 1
 
< 0.1%
101 87
 
0.4%
102 7
 
< 0.1%
103 8
 
< 0.1%
107 1
 
< 0.1%
109 227
0.9%
110 10
 
< 0.1%
111 41
 
0.2%
112 7
 
< 0.1%
113 143
0.6%
ValueCountFrequency (%)
999 6628
27.0%
931 482
 
2.0%
923 261
 
1.1%
922 6
 
< 0.1%
921 57
 
0.2%
910 68
 
0.3%
907 2
 
< 0.1%
716 1
 
< 0.1%
713 67
 
0.3%
711 5
 
< 0.1%
Distinct194
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
2023-07-12T17:34:24.933439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length70
Median length64
Mean length16.568198
Min length6

Characters and Unicode

Total characters407296
Distinct characters40
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.1%

Sample

1st rowOUTROS
2nd rowOUTROS
3rd rowTAXISTA
4th rowENGENHEIRO
5th rowCOMERCIANTE
ValueCountFrequency (%)
outros 6628
 
12.7%
de 5203
 
9.9%
e 3034
 
5.8%
agricultor 2447
 
4.7%
servidor 1956
 
3.7%
público 1956
 
3.7%
professor 1607
 
3.1%
ensino 1567
 
3.0%
assemelhados 1513
 
2.9%
municipal 1405
 
2.7%
Other values (328) 25069
47.9%
2023-07-12T17:34:25.746939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 46968
11.5%
E 41263
10.1%
R 39145
 
9.6%
A 32936
 
8.1%
S 30353
 
7.5%
I 28097
 
6.9%
27802
 
6.8%
T 25556
 
6.3%
D 18601
 
4.6%
C 16827
 
4.1%
Other values (30) 99748
24.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 376113
92.3%
Space Separator 27802
 
6.8%
Other Punctuation 2086
 
0.5%
Close Punctuation 527
 
0.1%
Open Punctuation 527
 
0.1%
Dash Punctuation 241
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 46968
12.5%
E 41263
11.0%
R 39145
10.4%
A 32936
8.8%
S 30353
 
8.1%
I 28097
 
7.5%
T 25556
 
6.8%
D 18601
 
4.9%
C 16827
 
4.5%
N 16135
 
4.3%
Other values (25) 80232
21.3%
Space Separator
ValueCountFrequency (%)
27802
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2086
100.0%
Close Punctuation
ValueCountFrequency (%)
) 527
100.0%
Open Punctuation
ValueCountFrequency (%)
( 527
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 241
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 376113
92.3%
Common 31183
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 46968
12.5%
E 41263
11.0%
R 39145
10.4%
A 32936
8.8%
S 30353
 
8.1%
I 28097
 
7.5%
T 25556
 
6.8%
D 18601
 
4.9%
C 16827
 
4.5%
N 16135
 
4.3%
Other values (25) 80232
21.3%
Common
ValueCountFrequency (%)
27802
89.2%
, 2086
 
6.7%
) 527
 
1.7%
( 527
 
1.7%
- 241
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 399750
98.1%
None 7546
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 46968
11.7%
E 41263
10.3%
R 39145
9.8%
A 32936
 
8.2%
S 30353
 
7.6%
I 28097
 
7.0%
27802
 
7.0%
T 25556
 
6.4%
D 18601
 
4.7%
C 16827
 
4.2%
Other values (19) 92202
23.1%
None
ValueCountFrequency (%)
Ú 2265
30.0%
Á 1813
24.0%
É 1251
16.6%
à 548
 
7.3%
Ç 482
 
6.4%
Í 479
 
6.3%
Ó 445
 
5.9%
 154
 
2.0%
Ô 52
 
0.7%
Õ 45
 
0.6%

VR_DESPESA_MAX_CAMPANHA
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59365.732
Minimum-4
Maximum438043.03
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size384.1 KiB
2023-07-12T17:34:26.073738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile12307.75
Q115568.96
median24713.5
Q353698.78
95-th percentile238660.11
Maximum438043.03
Range438047.03
Interquartile range (IQR)38129.82

Descriptive statistics

Standard deviation93041.751
Coefficient of variation (CV)1.5672636
Kurtosis9.4679249
Mean59365.732
Median Absolute Deviation (MAD)12405.75
Skewness3.1223139
Sum1.4593878 × 109
Variance8.6567674 × 109
MonotonicityNot monotonic
2023-07-12T17:34:26.361559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12307.75 5949
24.2%
438043.03 1083
 
4.4%
115386.04 654
 
2.7%
151743.76 528
 
2.1%
83396.64 488
 
2.0%
29099.54 441
 
1.8%
211925.15 427
 
1.7%
53698.78 421
 
1.7%
238660.11 349
 
1.4%
37269.99 321
 
1.3%
Other values (95) 13922
56.6%
ValueCountFrequency (%)
-4 1
 
< 0.1%
12307.75 5949
24.2%
15285.23 142
 
0.6%
15568.96 158
 
0.6%
15661.76 157
 
0.6%
15733.23 130
 
0.5%
15743.9 193
 
0.8%
15772.81 75
 
0.3%
15786.99 184
 
0.7%
15880.38 134
 
0.5%
ValueCountFrequency (%)
438043.03 1083
4.4%
238660.11 349
 
1.4%
211925.15 427
 
1.7%
151743.76 528
2.1%
130384.6 197
 
0.8%
115386.04 654
2.7%
106019.2 220
 
0.9%
91132.44 145
 
0.6%
87466.1 207
 
0.8%
83396.64 488
2.0%

CD_SIT_TOT_TURNO
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
5
14687 
4
7061 
2
 
1169
-1
 
897
3
 
769

Length

Max length2
Median length1
Mean length1.0364886
Min length1

Characters and Unicode

Total characters25480
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row5
4th row4
5th row2

Common Values

ValueCountFrequency (%)
5 14687
59.7%
4 7061
28.7%
2 1169
 
4.8%
-1 897
 
3.6%
3 769
 
3.1%

Length

2023-07-12T17:34:26.625398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:26.896231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
5 14687
59.7%
4 7061
28.7%
2 1169
 
4.8%
1 897
 
3.6%
3 769
 
3.1%

Most occurring characters

ValueCountFrequency (%)
5 14687
57.6%
4 7061
27.7%
2 1169
 
4.6%
- 897
 
3.5%
1 897
 
3.5%
3 769
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24583
96.5%
Dash Punctuation 897
 
3.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 14687
59.7%
4 7061
28.7%
2 1169
 
4.8%
1 897
 
3.6%
3 769
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25480
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 14687
57.6%
4 7061
27.7%
2 1169
 
4.6%
- 897
 
3.5%
1 897
 
3.5%
3 769
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 14687
57.6%
4 7061
27.7%
2 1169
 
4.6%
- 897
 
3.5%
1 897
 
3.5%
3 769
 
3.0%

DS_SIT_TOT_TURNO
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
SUPLENTE
14687 
NÃO ELEITO
7061 
ELEITO POR QP
 
1169
#NULO#
 
897
ELEITO POR MÉDIA
 
769

Length

Max length16
Median length8
Mean length8.9895049
Min length6

Characters and Unicode

Total characters220989
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUPLENTE
2nd rowNÃO ELEITO
3rd rowSUPLENTE
4th rowNÃO ELEITO
5th rowELEITO POR QP

Common Values

ValueCountFrequency (%)
SUPLENTE 14687
59.7%
NÃO ELEITO 7061
28.7%
ELEITO POR QP 1169
 
4.8%
#NULO# 897
 
3.6%
ELEITO POR MÉDIA 769
 
3.1%

Length

2023-07-12T17:34:27.143079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:27.420905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
suplente 14687
41.3%
eleito 8999
25.3%
não 7061
19.9%
por 1938
 
5.5%
qp 1169
 
3.3%
nulo 897
 
2.5%
média 769
 
2.2%

Most occurring characters

ValueCountFrequency (%)
E 47372
21.4%
L 24583
11.1%
T 23686
10.7%
N 22645
10.2%
O 18895
 
8.6%
P 17794
 
8.1%
U 15584
 
7.1%
S 14687
 
6.6%
10937
 
4.9%
I 9768
 
4.4%
Other values (8) 15038
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 208258
94.2%
Space Separator 10937
 
4.9%
Other Punctuation 1794
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 47372
22.7%
L 24583
11.8%
T 23686
11.4%
N 22645
10.9%
O 18895
 
9.1%
P 17794
 
8.5%
U 15584
 
7.5%
S 14687
 
7.1%
I 9768
 
4.7%
à 7061
 
3.4%
Other values (6) 6183
 
3.0%
Space Separator
ValueCountFrequency (%)
10937
100.0%
Other Punctuation
ValueCountFrequency (%)
# 1794
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 208258
94.2%
Common 12731
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 47372
22.7%
L 24583
11.8%
T 23686
11.4%
N 22645
10.9%
O 18895
 
9.1%
P 17794
 
8.5%
U 15584
 
7.5%
S 14687
 
7.1%
I 9768
 
4.7%
à 7061
 
3.4%
Other values (6) 6183
 
3.0%
Common
ValueCountFrequency (%)
10937
85.9%
# 1794
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 213159
96.5%
None 7830
 
3.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 47372
22.2%
L 24583
11.5%
T 23686
11.1%
N 22645
10.6%
O 18895
 
8.9%
P 17794
 
8.3%
U 15584
 
7.3%
S 14687
 
6.9%
10937
 
5.1%
I 9768
 
4.6%
Other values (6) 7208
 
3.4%
None
ValueCountFrequency (%)
à 7061
90.2%
É 769
 
9.8%

ST_REELEICAO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
N
23826 
S
 
756
Não divulgável
 
1

Length

Max length14
Median length1
Mean length1.0005288
Min length1

Characters and Unicode

Total characters24596
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 23826
96.9%
S 756
 
3.1%
Não divulgável 1
 
< 0.1%

Length

2023-07-12T17:34:27.667755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:27.931590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
n 23826
96.9%
s 756
 
3.1%
não 1
 
< 0.1%
divulgável 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 23827
96.9%
S 756
 
3.1%
v 2
 
< 0.1%
l 2
 
< 0.1%
ã 1
 
< 0.1%
o 1
 
< 0.1%
1
 
< 0.1%
d 1
 
< 0.1%
i 1
 
< 0.1%
u 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 24583
99.9%
Lowercase Letter 12
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 2
16.7%
l 2
16.7%
ã 1
8.3%
o 1
8.3%
d 1
8.3%
i 1
8.3%
u 1
8.3%
g 1
8.3%
á 1
8.3%
e 1
8.3%
Uppercase Letter
ValueCountFrequency (%)
N 23827
96.9%
S 756
 
3.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24595
> 99.9%
Common 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 23827
96.9%
S 756
 
3.1%
v 2
 
< 0.1%
l 2
 
< 0.1%
ã 1
 
< 0.1%
o 1
 
< 0.1%
d 1
 
< 0.1%
i 1
 
< 0.1%
u 1
 
< 0.1%
g 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24594
> 99.9%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 23827
96.9%
S 756
 
3.1%
v 2
 
< 0.1%
l 2
 
< 0.1%
o 1
 
< 0.1%
1
 
< 0.1%
d 1
 
< 0.1%
i 1
 
< 0.1%
u 1
 
< 0.1%
g 1
 
< 0.1%
None
ValueCountFrequency (%)
ã 1
50.0%
á 1
50.0%

ST_DECLARAR_BENS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
S
21110 
N
3472 
Não divulgável
 
1

Length

Max length14
Median length1
Mean length1.0005288
Min length1

Characters and Unicode

Total characters24596
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 21110
85.9%
N 3472
 
14.1%
Não divulgável 1
 
< 0.1%

Length

2023-07-12T17:34:28.148460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:28.406298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
s 21110
85.9%
n 3472
 
14.1%
não 1
 
< 0.1%
divulgável 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 21110
85.8%
N 3473
 
14.1%
v 2
 
< 0.1%
l 2
 
< 0.1%
ã 1
 
< 0.1%
o 1
 
< 0.1%
1
 
< 0.1%
d 1
 
< 0.1%
i 1
 
< 0.1%
u 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 24583
99.9%
Lowercase Letter 12
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 2
16.7%
l 2
16.7%
ã 1
8.3%
o 1
8.3%
d 1
8.3%
i 1
8.3%
u 1
8.3%
g 1
8.3%
á 1
8.3%
e 1
8.3%
Uppercase Letter
ValueCountFrequency (%)
S 21110
85.9%
N 3473
 
14.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24595
> 99.9%
Common 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 21110
85.8%
N 3473
 
14.1%
v 2
 
< 0.1%
l 2
 
< 0.1%
ã 1
 
< 0.1%
o 1
 
< 0.1%
d 1
 
< 0.1%
i 1
 
< 0.1%
u 1
 
< 0.1%
g 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24594
> 99.9%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 21110
85.8%
N 3473
 
14.1%
v 2
 
< 0.1%
l 2
 
< 0.1%
o 1
 
< 0.1%
1
 
< 0.1%
d 1
 
< 0.1%
i 1
 
< 0.1%
u 1
 
< 0.1%
g 1
 
< 0.1%
None
ValueCountFrequency (%)
ã 1
50.0%
á 1
50.0%

NR_PROTOCOLO_CANDIDATURA
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
-1
24583 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters49166
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 24583
100.0%

Length

2023-07-12T17:34:28.852025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:29.087881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 24583
100.0%

Most occurring characters

ValueCountFrequency (%)
- 24583
50.0%
1 24583
50.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 24583
50.0%
Decimal Number 24583
50.0%

Most frequent character per category

Dash Punctuation
ValueCountFrequency (%)
- 24583
100.0%
Decimal Number
ValueCountFrequency (%)
1 24583
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49166
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 24583
50.0%
1 24583
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 24583
50.0%
1 24583
50.0%

NR_PROCESSO
Real number (ℝ)

UNIQUE 

Distinct24583
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0027127 × 1018
Minimum6.0001678 × 1018
Maximum6.0165707 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.1 KiB
2023-07-12T17:34:29.323734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.0001678 × 1018
5-th percentile6.0007063 × 1018
Q16.0014419 × 1018
median6.0022993 × 1018
Q36.0036404 × 1018
95-th percentile6.0060022 × 1018
Maximum6.0165707 × 1018
Range1.64029 × 1016
Interquartile range (IQR)2.19845 × 1015

Descriptive statistics

Standard deviation1.7043434 × 1015
Coefficient of variation (CV)0.00028392886
Kurtosis2.3117801
Mean6.0027127 × 1018
Median Absolute Deviation (MAD)1.018 × 1015
Skewness1.2665983
Sum9.1806859 × 1018
Variance2.9047863 × 1030
MonotonicityNot monotonic
2023-07-12T17:34:29.640539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.00111082 × 10181
 
< 0.1%
6.00401322 × 10181
 
< 0.1%
6.00445182 × 10181
 
< 0.1%
6.00112182 × 10181
 
< 0.1%
6.00116272 × 10181
 
< 0.1%
6.00497572 × 10181
 
< 0.1%
6.00910412 × 10181
 
< 0.1%
6.00099882 × 10181
 
< 0.1%
6.00398762 × 10181
 
< 0.1%
6.00020302 × 10181
 
< 0.1%
Other values (24573) 24573
> 99.9%
ValueCountFrequency (%)
6.00016782 × 10181
< 0.1%
6.00017632 × 10181
< 0.1%
6.00018482 × 10181
< 0.1%
6.00019332 × 10181
< 0.1%
6.00019452 × 10181
< 0.1%
6.00019842 × 10181
< 0.1%
6.00020182 × 10181
< 0.1%
6.00020302 × 10181
< 0.1%
6.00020692 × 10181
< 0.1%
6.00021032 × 10181
< 0.1%
ValueCountFrequency (%)
6.01657072 × 10181
< 0.1%
6.01624982 × 10181
< 0.1%
6.01623162 × 10181
< 0.1%
6.01622312 × 10181
< 0.1%
6.01619922 × 10181
< 0.1%
6.01618102 × 10181
< 0.1%
6.01484932 × 10181
< 0.1%
6.01162312 × 10181
< 0.1%
6.01161462 × 10181
< 0.1%
6.01160612 × 10181
< 0.1%

CD_SITUACAO_CANDIDATO_PLEITO
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0559736
Minimum-1
Maximum20
Zeros0
Zeros (%)0.0%
Negative897
Negative (%)3.6%
Memory size384.1 KiB
2023-07-12T17:34:29.920367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum20
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.468889
Coefficient of variation (CV)0.71444933
Kurtosis63.970172
Mean2.0559736
Median Absolute Deviation (MAD)0
Skewness6.9896419
Sum50542
Variance2.1576349
MonotonicityNot monotonic
2023-07-12T17:34:30.212188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 23087
93.9%
-1 897
 
3.6%
4 303
 
1.2%
14 153
 
0.6%
16 72
 
0.3%
6 42
 
0.2%
17 22
 
0.1%
20 6
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
-1 897
 
3.6%
2 23087
93.9%
4 303
 
1.2%
6 42
 
0.2%
13 1
 
< 0.1%
14 153
 
0.6%
16 72
 
0.3%
17 22
 
0.1%
20 6
 
< 0.1%
ValueCountFrequency (%)
20 6
 
< 0.1%
17 22
 
0.1%
16 72
 
0.3%
14 153
 
0.6%
13 1
 
< 0.1%
6 42
 
0.2%
4 303
 
1.2%
2 23087
93.9%
-1 897
 
3.6%

DS_SITUACAO_CANDIDATO_PLEITO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
DEFERIDO
23087 
#NULO#
 
897
INDEFERIDO COM RECURSO
 
303
INDEFERIDO
 
153
DEFERIDO COM RECURSO
 
72
Other values (4)
 
71

Length

Max length32
Median length8
Mean length8.1660497
Min length6

Characters and Unicode

Total characters200746
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDEFERIDO
2nd rowDEFERIDO
3rd rowDEFERIDO
4th rowINDEFERIDO COM RECURSO
5th rowDEFERIDO

Common Values

ValueCountFrequency (%)
DEFERIDO 23087
93.9%
#NULO# 897
 
3.6%
INDEFERIDO COM RECURSO 303
 
1.2%
INDEFERIDO 153
 
0.6%
DEFERIDO COM RECURSO 72
 
0.3%
RENÚNCIA 42
 
0.2%
PENDENTE DE JULGAMENTO 22
 
0.1%
PEDIDO NÃO CONHECIDO COM RECURSO 6
 
< 0.1%
PEDIDO NÃO CONHECIDO 1
 
< 0.1%

Length

2023-07-12T17:34:30.461034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:30.765845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
deferido 23159
91.2%
nulo 897
 
3.5%
indeferido 456
 
1.8%
com 381
 
1.5%
recurso 381
 
1.5%
renúncia 42
 
0.2%
pendente 22
 
0.1%
de 22
 
0.1%
julgamento 22
 
0.1%
pedido 7
 
< 0.1%
Other values (2) 14
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 47777
23.8%
D 47295
23.6%
O 25324
12.6%
R 24419
12.2%
I 24127
12.0%
F 23615
11.8%
# 1794
 
0.9%
N 1517
 
0.8%
U 1300
 
0.6%
L 919
 
0.5%
Other values (12) 2659
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 198132
98.7%
Other Punctuation 1794
 
0.9%
Space Separator 820
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 47777
24.1%
D 47295
23.9%
O 25324
12.8%
R 24419
12.3%
I 24127
12.2%
F 23615
11.9%
N 1517
 
0.8%
U 1300
 
0.7%
L 919
 
0.5%
C 818
 
0.4%
Other values (10) 1021
 
0.5%
Other Punctuation
ValueCountFrequency (%)
# 1794
100.0%
Space Separator
ValueCountFrequency (%)
820
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 198132
98.7%
Common 2614
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 47777
24.1%
D 47295
23.9%
O 25324
12.8%
R 24419
12.3%
I 24127
12.2%
F 23615
11.9%
N 1517
 
0.8%
U 1300
 
0.7%
L 919
 
0.5%
C 818
 
0.4%
Other values (10) 1021
 
0.5%
Common
ValueCountFrequency (%)
# 1794
68.6%
820
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200697
> 99.9%
None 49
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 47777
23.8%
D 47295
23.6%
O 25324
12.6%
R 24419
12.2%
I 24127
12.0%
F 23615
11.8%
# 1794
 
0.9%
N 1517
 
0.8%
U 1300
 
0.6%
L 919
 
0.5%
Other values (10) 2610
 
1.3%
None
ValueCountFrequency (%)
Ú 42
85.7%
à 7
 
14.3%

CD_SITUACAO_CANDIDATO_URNA
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0886792
Minimum-1
Maximum20
Zeros0
Zeros (%)0.0%
Negative897
Negative (%)3.6%
Memory size384.1 KiB
2023-07-12T17:34:31.040677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum20
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.624685
Coefficient of variation (CV)0.77785282
Kurtosis65.482254
Mean2.0886792
Median Absolute Deviation (MAD)0
Skewness7.298939
Sum51346
Variance2.6396013
MonotonicityNot monotonic
2023-07-12T17:34:31.241553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 22858
93.0%
-1 897
 
3.6%
4 574
 
2.3%
17 139
 
0.6%
16 108
 
0.4%
20 7
 
< 0.1%
ValueCountFrequency (%)
-1 897
 
3.6%
2 22858
93.0%
4 574
 
2.3%
16 108
 
0.4%
17 139
 
0.6%
20 7
 
< 0.1%
ValueCountFrequency (%)
20 7
 
< 0.1%
17 139
 
0.6%
16 108
 
0.4%
4 574
 
2.3%
2 22858
93.0%
-1 897
 
3.6%

DS_SITUACAO_CANDIDATO_URNA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
DEFERIDO
22858 
#NULO#
 
897
INDEFERIDO COM RECURSO
 
574
PENDENTE DE JULGAMENTO
 
139
DEFERIDO COM RECURSO
 
108

Length

Max length32
Median length8
Mean length8.3926291
Min length6

Characters and Unicode

Total characters206316
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEFERIDO
2nd rowDEFERIDO
3rd rowDEFERIDO
4th rowINDEFERIDO COM RECURSO
5th rowDEFERIDO

Common Values

ValueCountFrequency (%)
DEFERIDO 22858
93.0%
#NULO# 897
 
3.6%
INDEFERIDO COM RECURSO 574
 
2.3%
PENDENTE DE JULGAMENTO 139
 
0.6%
DEFERIDO COM RECURSO 108
 
0.4%
PEDIDO NÃO CONHECIDO COM RECURSO 7
 
< 0.1%

Length

2023-07-12T17:34:31.477409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:31.758233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
deferido 22966
87.5%
nulo 897
 
3.4%
com 689
 
2.6%
recurso 689
 
2.6%
indeferido 574
 
2.2%
pendente 139
 
0.5%
de 139
 
0.5%
julgamento 139
 
0.5%
pedido 7
 
< 0.1%
não 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 48478
23.5%
D 47379
23.0%
O 25982
12.6%
R 24918
12.1%
I 24128
11.7%
F 23540
11.4%
N 1902
 
0.9%
# 1794
 
0.9%
U 1725
 
0.8%
1670
 
0.8%
Other values (11) 4800
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 202852
98.3%
Other Punctuation 1794
 
0.9%
Space Separator 1670
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 48478
23.9%
D 47379
23.4%
O 25982
12.8%
R 24918
12.3%
I 24128
11.9%
F 23540
11.6%
N 1902
 
0.9%
U 1725
 
0.9%
C 1392
 
0.7%
L 1036
 
0.5%
Other values (9) 2372
 
1.2%
Other Punctuation
ValueCountFrequency (%)
# 1794
100.0%
Space Separator
ValueCountFrequency (%)
1670
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 202852
98.3%
Common 3464
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 48478
23.9%
D 47379
23.4%
O 25982
12.8%
R 24918
12.3%
I 24128
11.9%
F 23540
11.6%
N 1902
 
0.9%
U 1725
 
0.9%
C 1392
 
0.7%
L 1036
 
0.5%
Other values (9) 2372
 
1.2%
Common
ValueCountFrequency (%)
# 1794
51.8%
1670
48.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 206309
> 99.9%
None 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 48478
23.5%
D 47379
23.0%
O 25982
12.6%
R 24918
12.1%
I 24128
11.7%
F 23540
11.4%
N 1902
 
0.9%
# 1794
 
0.9%
U 1725
 
0.8%
1670
 
0.8%
Other values (10) 4793
 
2.3%
None
ValueCountFrequency (%)
à 7
100.0%

ST_CANDIDATO_INSERIDO_URNA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
SIM
23687 
NÃO
 
896

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters73749
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSIM
2nd rowSIM
3rd rowSIM
4th rowSIM
5th rowSIM

Common Values

ValueCountFrequency (%)
SIM 23687
96.4%
NÃO 896
 
3.6%

Length

2023-07-12T17:34:32.042061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:32.285129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sim 23687
96.4%
não 896
 
3.6%

Most occurring characters

ValueCountFrequency (%)
S 23687
32.1%
I 23687
32.1%
M 23687
32.1%
N 896
 
1.2%
à 896
 
1.2%
O 896
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 73749
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 23687
32.1%
I 23687
32.1%
M 23687
32.1%
N 896
 
1.2%
à 896
 
1.2%
O 896
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 73749
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 23687
32.1%
I 23687
32.1%
M 23687
32.1%
N 896
 
1.2%
à 896
 
1.2%
O 896
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72853
98.8%
None 896
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 23687
32.5%
I 23687
32.5%
M 23687
32.5%
N 896
 
1.2%
O 896
 
1.2%
None
ValueCountFrequency (%)
à 896
100.0%

NM_TIPO_DESTINACAO_VOTOS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
Válido
23088 
#NULO#
 
897
Anulado
 
213
Nulo técnico
 
191
Anulado sub judice
 
187

Length

Max length18
Median length6
Mean length6.1494122
Min length6

Characters and Unicode

Total characters151171
Distinct characters26
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVálido
2nd rowVálido
3rd rowVálido
4th rowAnulado sub judice
5th rowVálido

Common Values

ValueCountFrequency (%)
Válido 23088
93.9%
#NULO# 897
 
3.6%
Anulado 213
 
0.9%
Nulo técnico 191
 
0.8%
Anulado sub judice 187
 
0.8%
Válido (legenda) 7
 
< 0.1%

Length

2023-07-12T17:34:32.507992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:32.892755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
válido 23095
91.8%
nulo 1088
 
4.3%
anulado 400
 
1.6%
técnico 191
 
0.8%
sub 187
 
0.7%
judice 187
 
0.7%
legenda 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 23877
15.8%
l 23693
15.7%
d 23689
15.7%
i 23473
15.5%
V 23095
15.3%
á 23095
15.3%
# 1794
 
1.2%
N 1088
 
0.7%
u 965
 
0.6%
L 897
 
0.6%
Other values (16) 5505
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 121517
80.4%
Uppercase Letter 27274
 
18.0%
Other Punctuation 1794
 
1.2%
Space Separator 572
 
0.4%
Open Punctuation 7
 
< 0.1%
Close Punctuation 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 23877
19.6%
l 23693
19.5%
d 23689
19.5%
i 23473
19.3%
á 23095
19.0%
u 965
 
0.8%
n 598
 
0.5%
c 569
 
0.5%
a 407
 
0.3%
e 201
 
0.2%
Other values (6) 950
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
V 23095
84.7%
N 1088
 
4.0%
L 897
 
3.3%
U 897
 
3.3%
O 897
 
3.3%
A 400
 
1.5%
Other Punctuation
ValueCountFrequency (%)
# 1794
100.0%
Space Separator
ValueCountFrequency (%)
572
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 148791
98.4%
Common 2380
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 23877
16.0%
l 23693
15.9%
d 23689
15.9%
i 23473
15.8%
V 23095
15.5%
á 23095
15.5%
N 1088
 
0.7%
u 965
 
0.6%
L 897
 
0.6%
U 897
 
0.6%
Other values (12) 4022
 
2.7%
Common
ValueCountFrequency (%)
# 1794
75.4%
572
 
24.0%
( 7
 
0.3%
) 7
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127885
84.6%
None 23286
 
15.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 23877
18.7%
l 23693
18.5%
d 23689
18.5%
i 23473
18.4%
V 23095
18.1%
# 1794
 
1.4%
N 1088
 
0.9%
u 965
 
0.8%
L 897
 
0.7%
U 897
 
0.7%
Other values (14) 4417
 
3.5%
None
ValueCountFrequency (%)
á 23095
99.2%
é 191
 
0.8%

CD_SITUACAO_CANDIDATO_TOT
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1063743
Minimum-1
Maximum20
Zeros0
Zeros (%)0.0%
Negative897
Negative (%)3.6%
Memory size384.1 KiB
2023-07-12T17:34:33.169583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum20
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6186162
Coefficient of variation (CV)0.76843709
Kurtosis44.538908
Mean2.1063743
Median Absolute Deviation (MAD)0
Skewness5.9630171
Sum51781
Variance2.6199183
MonotonicityNot monotonic
2023-07-12T17:34:33.389446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 23032
93.7%
-1 897
 
3.6%
14 232
 
0.9%
4 181
 
0.7%
10 137
 
0.6%
16 52
 
0.2%
6 42
 
0.2%
20 6
 
< 0.1%
17 4
 
< 0.1%
ValueCountFrequency (%)
-1 897
 
3.6%
2 23032
93.7%
4 181
 
0.7%
6 42
 
0.2%
10 137
 
0.6%
14 232
 
0.9%
16 52
 
0.2%
17 4
 
< 0.1%
20 6
 
< 0.1%
ValueCountFrequency (%)
20 6
 
< 0.1%
17 4
 
< 0.1%
16 52
 
0.2%
14 232
 
0.9%
10 137
 
0.6%
6 42
 
0.2%
4 181
 
0.7%
2 23032
93.7%
-1 897
 
3.6%

DS_SITUACAO_CANDIDATO_TOT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
Deferido
23032 
#NULO#
 
897
Indeferido
 
232
Indeferido com recurso
 
181
Cassado
 
137
Other values (4)
 
104

Length

Max length32
Median length8
Mean length8.0769231
Min length6

Characters and Unicode

Total characters198555
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeferido
2nd rowDeferido
3rd rowDeferido
4th rowIndeferido com recurso
5th rowDeferido

Common Values

ValueCountFrequency (%)
Deferido 23032
93.7%
#NULO# 897
 
3.6%
Indeferido 232
 
0.9%
Indeferido com recurso 181
 
0.7%
Cassado 137
 
0.6%
Deferido com recurso 52
 
0.2%
Renúncia 42
 
0.2%
Pedido não conhecido com recurso 6
 
< 0.1%
Pendente de julgamento 4
 
< 0.1%

Length

2023-07-12T17:34:33.668277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:33.974088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
deferido 23084
92.0%
nulo 897
 
3.6%
indeferido 413
 
1.6%
com 239
 
1.0%
recurso 239
 
1.0%
cassado 137
 
0.5%
renúncia 42
 
0.2%
pedido 6
 
< 0.1%
não 6
 
< 0.1%
conhecido 6
 
< 0.1%
Other values (3) 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 47307
23.8%
o 24140
12.2%
d 24073
12.1%
r 23975
12.1%
i 23551
11.9%
f 23497
11.8%
D 23084
11.6%
# 1794
 
0.9%
N 897
 
0.5%
U 897
 
0.5%
Other values (20) 5340
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 168989
85.1%
Uppercase Letter 27274
 
13.7%
Other Punctuation 1794
 
0.9%
Space Separator 498
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 47307
28.0%
o 24140
14.3%
d 24073
14.2%
r 23975
14.2%
i 23551
13.9%
f 23497
13.9%
c 532
 
0.3%
n 521
 
0.3%
s 513
 
0.3%
a 320
 
0.2%
Other values (9) 560
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
D 23084
84.6%
N 897
 
3.3%
U 897
 
3.3%
L 897
 
3.3%
O 897
 
3.3%
I 413
 
1.5%
C 137
 
0.5%
R 42
 
0.2%
P 10
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
# 1794
100.0%
Space Separator
ValueCountFrequency (%)
498
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 196263
98.8%
Common 2292
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 47307
24.1%
o 24140
12.3%
d 24073
12.3%
r 23975
12.2%
i 23551
12.0%
f 23497
12.0%
D 23084
11.8%
N 897
 
0.5%
U 897
 
0.5%
L 897
 
0.5%
Other values (18) 3945
 
2.0%
Common
ValueCountFrequency (%)
# 1794
78.3%
498
 
21.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 198507
> 99.9%
None 48
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 47307
23.8%
o 24140
12.2%
d 24073
12.1%
r 23975
12.1%
i 23551
11.9%
f 23497
11.8%
D 23084
11.6%
# 1794
 
0.9%
N 897
 
0.5%
U 897
 
0.5%
Other values (18) 5292
 
2.7%
None
ValueCountFrequency (%)
ú 42
87.5%
ã 6
 
12.5%

ST_PREST_CONTAS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size384.1 KiB
S
23434 
N
 
1149

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24583
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 23434
95.3%
N 1149
 
4.7%

Length

2023-07-12T17:34:34.262912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:34:34.501763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
s 23434
95.3%
n 1149
 
4.7%

Most occurring characters

ValueCountFrequency (%)
S 23434
95.3%
N 1149
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 24583
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 23434
95.3%
N 1149
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 24583
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 23434
95.3%
N 1149
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24583
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 23434
95.3%
N 1149
 
4.7%

Interactions

2023-07-12T17:33:44.749901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:01.901606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:07.844945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:14.547817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:21.234695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:27.806648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:34.387595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:40.458854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:46.716997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:53.105063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:59.108364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:06.002118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:12.552082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:18.791237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:24.763557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:29.860928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:34.856395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:39.685421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:45.005741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:02.348334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:08.138766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:14.863622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:21.575487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:28.159429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:34.732381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:40.765664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:47.041797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:53.445851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:59.483132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:06.305929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:12.948835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:19.198984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:25.003410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:30.127760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:35.090251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:39.946259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:45.263583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:02.741090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:08.447577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:15.198414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:21.968244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:28.507217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:35.062180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:41.085467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:47.415568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:53.795636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:59.841916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:06.638725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:13.281629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:19.541775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:25.482115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:30.417581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:35.351089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:40.234082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:45.549408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:03.100870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:08.793363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:15.558194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:22.370996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:28.906971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:35.448939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:41.665111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:47.767349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:54.141421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:00.237667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:06.988506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-12T17:33:33.824031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:38.569108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:43.699545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:49.544959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:06.920515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:13.539438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:19.911512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:26.799269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:33.343237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:39.510437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:45.726608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:52.079695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:58.172941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:04.969753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:11.297854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:17.680921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:23.834130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:29.096394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:34.092866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:38.916894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:44.005356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:49.804784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:07.245315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:13.935193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:20.268293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:27.152052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:33.727999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:39.834239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:46.055405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:52.423482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:58.487744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:05.311541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:11.690610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:17.988731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:24.147937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:29.355233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:34.353703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:39.201716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:44.256202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:50.057630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:07.545130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:14.234013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:20.600089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:27.493841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:34.034811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:40.141048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:46.390200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:52.763272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:32:58.787561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:05.649335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:12.111350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:18.381495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:24.468741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:29.603082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:34.605549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:39.453562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:33:44.512045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-12T17:34:34.848165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
SG_UESQ_CANDIDATONR_CANDIDATONR_CPF_CANDIDATOCD_DETALHE_SITUACAO_CANDNR_PARTIDOSQ_COLIGACAONR_IDADE_DATA_POSSENR_TITULO_ELEITORAL_CANDIDATOCD_GRAU_INSTRUCAOCD_ESTADO_CIVILCD_COR_RACACD_OCUPACAOVR_DESPESA_MAX_CAMPANHANR_PROCESSOCD_SITUACAO_CANDIDATO_PLEITOCD_SITUACAO_CANDIDATO_URNACD_SITUACAO_CANDIDATO_TOTCD_ELEICAODS_ELEICAOSG_UFNM_SOCIAL_CANDIDATOCD_SITUACAO_CANDIDATURADS_SITUACAO_CANDIDATURADS_DETALHE_SITUACAO_CANDSG_PARTIDONM_PARTIDODS_COMPOSICAO_COLIGACAOCD_NACIONALIDADEDS_NACIONALIDADESG_UF_NASCIMENTOCD_GENERODS_GENERODS_GRAU_INSTRUCAODS_ESTADO_CIVILDS_COR_RACACD_SIT_TOT_TURNODS_SIT_TOT_TURNOST_REELEICAOST_DECLARAR_BENSDS_SITUACAO_CANDIDATO_PLEITODS_SITUACAO_CANDIDATO_URNAST_CANDIDATO_INSERIDO_URNANM_TIPO_DESTINACAO_VOTOSDS_SITUACAO_CANDIDATO_TOTST_PREST_CONTAS
SG_UE1.000-0.231-0.0200.0060.010-0.019-0.231-0.036-0.1910.006-0.018-0.0070.027-0.128-0.009-0.004-0.007-0.0040.4170.4170.9050.0000.0380.0380.0520.1250.1250.1250.0330.0330.2480.0000.0000.0470.0380.0520.0380.0380.0150.0630.0360.0600.0370.0510.0530.049
SQ_CANDIDATO-0.2311.000-0.007-0.0120.057-0.0060.9800.0050.216-0.0710.012-0.0200.0160.0260.4110.0120.0270.0110.4610.4611.0000.0080.0000.0000.0910.2470.2470.2470.0410.0410.7550.0200.0200.0900.0650.0490.0310.0310.0230.0290.0400.0320.0230.0670.0670.033
NR_CANDIDATO-0.020-0.0071.0000.0050.0240.999-0.007-0.0350.0240.015-0.0190.0050.0060.0540.013-0.009-0.0130.0060.0580.0580.0700.0080.0780.0780.0640.9960.9960.9960.0250.0250.0210.0000.0000.0200.0240.0410.1030.1030.0270.0210.0390.0500.0410.0860.0710.058
NR_CPF_CANDIDATO0.006-0.0120.0051.000-0.0130.005-0.008-0.1280.0680.009-0.0320.023-0.037-0.037-0.0300.0070.0090.0070.0300.0300.0380.0000.0250.0250.0100.0230.0230.0230.0000.0000.0500.0730.0730.0430.1050.0150.0460.0460.0470.0310.0040.0170.0220.0110.0090.027
CD_DETALHE_SITUACAO_CAND0.0100.0570.024-0.0131.0000.0240.0480.019-0.012-0.052-0.0120.0050.0330.0200.043-0.273-0.272-0.1830.0170.0170.0880.0000.9970.9971.0000.0840.0840.0840.0140.0140.0680.0250.0250.0300.0170.0130.4320.4320.0290.0470.6320.6480.8430.6520.8140.764
NR_PARTIDO-0.019-0.0060.9990.0050.0241.000-0.007-0.0360.0250.016-0.0190.0050.0050.0540.014-0.009-0.0130.0060.0570.0570.0700.0070.0790.0790.0641.0001.0001.0000.0250.0250.0240.0000.0000.0200.0260.0410.1020.1020.0260.0210.0390.0510.0410.0850.0710.058
SQ_COLIGACAO-0.2310.980-0.007-0.0080.048-0.0071.0000.0100.211-0.0690.014-0.0180.0110.0260.3980.0080.0170.0100.4610.4611.0000.0080.0000.0000.0910.2470.2470.2470.0410.0410.7550.0200.0200.0900.0650.0490.0310.0310.0230.0290.0400.0320.0230.0670.0670.033
NR_IDADE_DATA_POSSE-0.0360.005-0.035-0.1280.019-0.0360.0101.000-0.708-0.0930.306-0.024-0.0690.0710.017-0.007-0.006-0.0040.0230.0230.0310.0000.0350.0350.0160.0340.0340.0340.0040.0040.1410.1000.1000.0810.1700.0190.0490.0490.0610.0530.0100.0140.0280.0130.0070.027
NR_TITULO_ELEITORAL_CANDIDATO-0.1910.2160.0240.068-0.0120.0250.211-0.7081.000-0.020-0.1950.0010.070-0.006-0.014-0.000-0.000-0.0020.0960.0960.2070.0000.0000.0000.0000.0250.0250.0250.0000.0000.1380.0450.0450.0340.0860.0120.0330.0330.0340.0230.0000.0000.0000.0000.0060.000
CD_GRAU_INSTRUCAO0.006-0.0710.0150.009-0.0520.016-0.069-0.093-0.0201.0000.056-0.038-0.2930.1650.0550.0380.0340.0420.1160.1160.0850.4080.0560.0560.0310.0390.0390.0390.5770.5770.4120.7110.7111.0000.4480.4090.0580.0580.7070.7080.0320.0380.0700.0320.0330.074
CD_ESTADO_CIVIL-0.0180.012-0.019-0.032-0.012-0.0190.0140.306-0.1950.0561.000-0.035-0.0860.0910.0250.0130.0050.0150.0460.0460.0650.4470.0340.0340.0160.0450.0450.0450.5780.5780.4540.7130.7130.4501.0000.4480.0410.0410.7090.7080.0200.0210.0390.0180.0300.041
CD_COR_RACA-0.007-0.0200.0050.0230.0050.005-0.018-0.0240.001-0.038-0.0351.0000.058-0.065-0.0350.0080.0100.0100.0500.0500.0490.4080.0280.0280.0110.0740.0740.0740.5790.5790.4170.7080.7080.4100.4481.0000.0320.0320.7070.7080.0190.0260.0360.0170.0160.032
CD_OCUPACAO0.0270.0160.006-0.0370.0330.0050.011-0.0690.070-0.293-0.0860.0581.000-0.0410.011-0.025-0.022-0.0320.0850.0850.0950.3540.0510.0510.0270.0610.0610.0610.5770.5770.3560.7130.7130.3890.4500.4100.1020.1020.7250.7090.0270.0350.0640.0300.0300.067
VR_DESPESA_MAX_CAMPANHA-0.1280.0260.054-0.0370.0200.0540.0260.071-0.0060.1650.091-0.065-0.0411.0000.2750.0080.0120.0080.9990.9990.4710.0000.0360.0360.0330.1240.1240.1240.0200.0200.1810.0000.0000.0850.0480.0540.0680.0680.0190.0730.0340.0460.0270.0410.0420.039
NR_PROCESSO-0.0090.4110.013-0.0300.0430.0140.3980.017-0.0140.0550.025-0.0350.0110.2751.0000.0010.008-0.0130.2810.2810.1220.0180.0500.0500.0400.1050.1050.1050.0440.0440.0500.0510.0510.0330.0420.0400.0790.0790.0610.0540.0520.0880.0440.0480.0840.058
CD_SITUACAO_CANDIDATO_PLEITO-0.0040.012-0.0090.007-0.273-0.0090.008-0.007-0.0000.0380.0130.008-0.0250.0080.0011.0000.9050.9330.0250.0250.0400.0000.9110.9110.6200.0560.0560.0560.0110.0110.0580.0220.0220.0290.0160.0150.5100.5100.0310.0471.0000.7730.9990.7400.8330.882
CD_SITUACAO_CANDIDATO_URNA-0.0070.027-0.0130.009-0.272-0.0130.017-0.006-0.0000.0340.0050.010-0.0220.0120.0080.9051.0000.8560.0280.0280.0310.0000.8620.8620.7090.0720.0720.0720.0150.0150.0720.0250.0250.0420.0210.0230.5090.5090.0300.0500.8451.0000.9990.6280.8240.881
CD_SITUACAO_CANDIDATO_TOT-0.0040.0110.0060.007-0.1830.0060.010-0.004-0.0020.0420.0150.010-0.0320.008-0.0130.9330.8561.0000.0290.0290.0670.0000.9810.9810.7730.0880.0880.0880.0110.0110.0650.0220.0220.0300.0180.0170.5150.5150.0330.0450.8400.7640.9990.8361.0000.882
CD_ELEICAO0.4170.4610.0580.0300.0170.0570.4610.0230.0960.1160.0460.0500.0850.9990.2810.0250.0280.0291.0000.9990.4610.0390.0090.0090.0260.1310.1310.1310.0420.0420.3670.0420.0420.1190.0460.0500.0410.0410.0460.0440.0250.0270.0250.0270.0340.028
DS_ELEICAO0.4170.4610.0580.0300.0170.0570.4610.0230.0960.1160.0460.0500.0850.9990.2810.0250.0280.0290.9991.0000.4610.0390.0090.0090.0260.1310.1310.1310.0420.0420.3670.0420.0420.1190.0460.0500.0410.0410.0460.0440.0250.0270.0250.0270.0340.028
SG_UF0.9051.0000.0700.0380.0880.0701.0000.0310.2070.0850.0650.0490.0950.4710.1220.0400.0310.0670.4610.4611.0000.0080.0000.0000.0910.2470.2470.2470.0410.0410.7550.0200.0200.0900.0650.0490.0310.0310.0230.0290.0400.0320.0230.0670.0670.033
NM_SOCIAL_CANDIDATO0.0000.0080.0080.0000.0000.0070.0080.0000.0000.4080.4470.4080.3540.0000.0180.0000.0000.0000.0390.0390.0081.0000.0180.0180.0000.0000.0000.0000.5770.5770.3350.7070.7070.3530.4470.4080.0140.0140.7070.7070.0000.0000.0270.0000.0000.022
CD_SITUACAO_CANDIDATURA0.0380.0000.0780.0250.9970.0790.0000.0350.0000.0560.0340.0280.0510.0360.0500.9110.8620.9810.0090.0090.0000.0181.0001.0001.0000.1000.1000.1000.0240.0240.0140.0280.0280.0580.0340.0280.8090.8090.0400.0590.9110.8630.7970.9810.9810.720
DS_SITUACAO_CANDIDATURA0.0380.0000.0780.0250.9970.0790.0000.0350.0000.0560.0340.0280.0510.0360.0500.9110.8620.9810.0090.0090.0000.0181.0001.0001.0000.1000.1000.1000.0240.0240.0140.0280.0280.0580.0340.0280.8090.8090.0400.0590.9110.8630.7970.9810.9810.720
DS_DETALHE_SITUACAO_CAND0.0520.0910.0640.0101.0000.0640.0910.0160.0000.0310.0160.0110.0270.0330.0400.6200.7090.7730.0260.0260.0910.0001.0001.0001.0000.0720.0720.0720.0090.0090.0560.0250.0250.0280.0160.0110.4350.4350.0310.0470.5950.6510.8480.6590.7650.769
SG_PARTIDO0.1250.2470.9960.0230.0841.0000.2470.0340.0250.0390.0450.0740.0610.1240.1050.0560.0720.0880.1310.1310.2470.0000.1000.1000.0721.0001.0001.0000.0360.0360.0420.0000.0000.0400.0450.0740.1980.1980.0680.0670.0530.0700.0820.0960.0820.105
NM_PARTIDO0.1250.2470.9960.0230.0841.0000.2470.0340.0250.0390.0450.0740.0610.1240.1050.0560.0720.0880.1310.1310.2470.0000.1000.1000.0721.0001.0001.0000.0360.0360.0420.0000.0000.0400.0450.0740.1980.1980.0680.0670.0530.0700.0820.0960.0820.105
DS_COMPOSICAO_COLIGACAO0.1250.2470.9960.0230.0841.0000.2470.0340.0250.0390.0450.0740.0610.1240.1050.0560.0720.0880.1310.1310.2470.0000.1000.1000.0721.0001.0001.0000.0360.0360.0420.0000.0000.0400.0450.0740.1980.1980.0680.0670.0530.0700.0820.0960.0820.105
CD_NACIONALIDADE0.0330.0410.0250.0000.0140.0250.0410.0040.0000.5770.5780.5790.5770.0200.0440.0110.0150.0110.0420.0420.0410.5770.0240.0240.0090.0360.0360.0361.0001.0000.6230.7070.7070.5770.5780.5790.0180.0180.7070.7070.0090.0140.0320.0140.0090.027
DS_NACIONALIDADE0.0330.0410.0250.0000.0140.0250.0410.0040.0000.5770.5780.5790.5770.0200.0440.0110.0150.0110.0420.0420.0410.5770.0240.0240.0090.0360.0360.0361.0001.0000.6230.7070.7070.5770.5780.5790.0180.0180.7070.7070.0090.0140.0320.0140.0090.027
SG_UF_NASCIMENTO0.2480.7550.0210.0500.0680.0240.7550.1410.1380.4120.4540.4170.3560.1810.0500.0580.0720.0650.3670.3670.7550.3350.0140.0140.0560.0420.0420.0420.6230.6231.0000.7070.7070.3570.4540.4170.0350.0350.7080.7080.0550.0650.0330.0320.0610.042
CD_GENERO0.0000.0200.0000.0730.0250.0000.0200.1000.0450.7110.7130.7080.7130.0000.0510.0220.0250.0220.0420.0420.0200.7070.0280.0280.0250.0000.0000.0000.7070.7070.7071.0001.0000.7110.7130.7080.0830.0830.7090.7080.0220.0260.0340.0230.0210.032
DS_GENERO0.0000.0200.0000.0730.0250.0000.0200.1000.0450.7110.7130.7080.7130.0000.0510.0220.0250.0220.0420.0420.0200.7070.0280.0280.0250.0000.0000.0000.7070.7070.7071.0001.0000.7110.7130.7080.0830.0830.7090.7080.0220.0260.0340.0230.0210.032
DS_GRAU_INSTRUCAO0.0470.0900.0200.0430.0300.0200.0900.0810.0341.0000.4500.4100.3890.0850.0330.0290.0420.0300.1190.1190.0900.3530.0580.0580.0280.0400.0400.0400.5770.5770.3570.7110.7111.0000.4500.4100.0600.0600.7070.7080.0270.0390.0730.0320.0290.075
DS_ESTADO_CIVIL0.0380.0650.0240.1050.0170.0260.0650.1700.0860.4481.0000.4480.4500.0480.0420.0160.0210.0180.0460.0460.0650.4470.0340.0340.0160.0450.0450.0450.5780.5780.4540.7130.7130.4501.0000.4480.0410.0410.7090.7080.0200.0210.0390.0180.0300.041
DS_COR_RACA0.0520.0490.0410.0150.0130.0410.0490.0190.0120.4090.4481.0000.4100.0540.0400.0150.0230.0170.0500.0500.0490.4080.0280.0280.0110.0740.0740.0740.5790.5790.4170.7080.7080.4100.4481.0000.0320.0320.7070.7080.0190.0260.0360.0170.0160.032
CD_SIT_TOT_TURNO0.0380.0310.1030.0460.4320.1020.0310.0490.0330.0580.0410.0320.1020.0680.0790.5100.5090.5150.0410.0410.0310.0140.8090.8090.4350.1980.1980.1980.0180.0180.0350.0830.0830.0600.0410.0321.0001.0000.1760.0600.5100.5090.9990.5150.5150.880
DS_SIT_TOT_TURNO0.0380.0310.1030.0460.4320.1020.0310.0490.0330.0580.0410.0320.1020.0680.0790.5100.5090.5150.0410.0410.0310.0140.8090.8090.4350.1980.1980.1980.0180.0180.0350.0830.0830.0600.0410.0321.0001.0000.1760.0600.5100.5090.9990.5150.5150.880
ST_REELEICAO0.0150.0230.0270.0470.0290.0260.0230.0610.0340.7070.7090.7070.7250.0190.0610.0310.0300.0330.0460.0460.0230.7070.0400.0400.0310.0680.0680.0680.7070.7070.7080.7090.7090.7070.7090.7070.1760.1761.0000.7070.0310.0320.0420.0300.0330.043
ST_DECLARAR_BENS0.0630.0290.0210.0310.0470.0210.0290.0530.0230.7080.7080.7080.7090.0730.0540.0470.0500.0450.0440.0440.0290.7070.0590.0590.0470.0670.0670.0670.7070.7070.7080.7080.7080.7080.7080.7080.0600.0600.7071.0000.0470.0530.0630.0480.0450.072
DS_SITUACAO_CANDIDATO_PLEITO0.0360.0400.0390.0040.6320.0390.0400.0100.0000.0320.0200.0190.0270.0340.0521.0000.8450.8400.0250.0250.0400.0000.9110.9110.5950.0530.0530.0530.0090.0090.0550.0220.0220.0270.0200.0190.5100.5100.0310.0471.0000.8030.9990.7420.8030.882
DS_SITUACAO_CANDIDATO_URNA0.0600.0320.0500.0170.6480.0510.0320.0140.0000.0380.0210.0260.0350.0460.0880.7731.0000.7640.0270.0270.0320.0000.8630.8630.6510.0700.0700.0700.0140.0140.0650.0260.0260.0390.0210.0260.5090.5090.0320.0530.8031.0000.9990.5680.7710.881
ST_CANDIDATO_INSERIDO_URNA0.0370.0230.0410.0220.8430.0410.0230.0280.0000.0700.0390.0360.0640.0270.0440.9990.9990.9990.0250.0250.0230.0270.7970.7970.8480.0820.0820.0820.0320.0320.0330.0340.0340.0730.0390.0360.9990.9990.0420.0630.9990.9991.0000.9990.9990.878
NM_TIPO_DESTINACAO_VOTOS0.0510.0670.0860.0110.6520.0850.0670.0130.0000.0320.0180.0170.0300.0410.0480.7400.6280.8360.0270.0270.0670.0000.9810.9810.6590.0960.0960.0960.0140.0140.0320.0230.0230.0320.0180.0170.5150.5150.0300.0480.7420.5680.9991.0000.8360.880
DS_SITUACAO_CANDIDATO_TOT0.0530.0670.0710.0090.8140.0710.0670.0070.0060.0330.0300.0160.0300.0420.0840.8330.8241.0000.0340.0340.0670.0000.9810.9810.7650.0820.0820.0820.0090.0090.0610.0210.0210.0290.0300.0160.5150.5150.0330.0450.8030.7710.9990.8361.0000.882
ST_PREST_CONTAS0.0490.0330.0580.0270.7640.0580.0330.0270.0000.0740.0410.0320.0670.0390.0580.8820.8810.8820.0280.0280.0330.0220.7200.7200.7690.1050.1050.1050.0270.0270.0420.0320.0320.0750.0410.0320.8800.8800.0430.0720.8820.8810.8780.8800.8821.000

Missing values

2023-07-12T17:33:50.898600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-12T17:33:52.162241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-12T17:33:53.642440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DT_GERACAOHH_GERACAOANO_ELEICAOCD_TIPO_ELEICAONM_TIPO_ELEICAONR_TURNOCD_ELEICAODS_ELEICAODT_ELEICAOTP_ABRANGENCIASG_UFSG_UENM_UECD_CARGODS_CARGOSQ_CANDIDATONR_CANDIDATONM_CANDIDATONM_URNA_CANDIDATONM_SOCIAL_CANDIDATONR_CPF_CANDIDATONM_EMAILCD_SITUACAO_CANDIDATURADS_SITUACAO_CANDIDATURACD_DETALHE_SITUACAO_CANDDS_DETALHE_SITUACAO_CANDTP_AGREMIACAONR_PARTIDOSG_PARTIDONM_PARTIDONR_FEDERACAONM_FEDERACAOSG_FEDERACAODS_COMPOSICAO_FEDERACAOSQ_COLIGACAONM_COLIGACAODS_COMPOSICAO_COLIGACAOCD_NACIONALIDADEDS_NACIONALIDADESG_UF_NASCIMENTOCD_MUNICIPIO_NASCIMENTONM_MUNICIPIO_NASCIMENTODT_NASCIMENTONR_IDADE_DATA_POSSENR_TITULO_ELEITORAL_CANDIDATOCD_GENERODS_GENEROCD_GRAU_INSTRUCAODS_GRAU_INSTRUCAOCD_ESTADO_CIVILDS_ESTADO_CIVILCD_COR_RACADS_COR_RACACD_OCUPACAODS_OCUPACAOVR_DESPESA_MAX_CAMPANHACD_SIT_TOT_TURNODS_SIT_TOT_TURNOST_REELEICAOST_DECLARAR_BENSNR_PROTOCOLO_CANDIDATURANR_PROCESSOCD_SITUACAO_CANDIDATO_PLEITODS_SITUACAO_CANDIDATO_PLEITOCD_SITUACAO_CANDIDATO_URNADS_SITUACAO_CANDIDATO_URNAST_CANDIDATO_INSERIDO_URNANM_TIPO_DESTINACAO_VOTOSCD_SITUACAO_CANDIDATO_TOTDS_SITUACAO_CANDIDATO_TOTST_PREST_CONTAS
006/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALAP6114FERREIRA GOMES13VEREADOR3000067704811369RAIMUNDO MESQUITA FERREIRA DOS SANTOSGOIABA#NULO#65078519220NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO11PPPROGRESSISTAS-1#NULO##NULO##NULO#30000061001PARTIDO ISOLADOPP1BRASILEIRA NATAAP-3AMAPÁ28/01/197644.018854825002MASCULINO6ENSINO MÉDIO COMPLETO1SOLTEIRO(A)2PRETA999OUTROS12307.755SUPLENTENS-160011108202060300122DEFERIDO2DEFERIDOSIMVálido2DeferidoS
106/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALAP6076MAZAGÃO13VEREADOR3000078009513000MAURICIO DEL CASTILLO RAIOLMAURICIO DA SAÚDE#NULO#6246915272NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO13PTPARTIDO DOS TRABALHADORES-1#NULO##NULO##NULO#30000075729PARTIDO ISOLADOPT1BRASILEIRA NATAPA-3BELÉM20/03/195763.02299325852MASCULINO6ENSINO MÉDIO COMPLETO3CASADO(A)3PARDA999OUTROS24149.884NÃO ELEITONS-160010352202060300052DEFERIDO2DEFERIDOSIMVálido2DeferidoS
206/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALAP6106SERRA DO NAVIO13VEREADOR3000069851855123KENNAS DE OLIVEIRA DOS SANTOSNEGUINHO DO TAXI#NULO#62275968253NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO55PSDPARTIDO SOCIAL DEMOCRÁTICO-1#NULO##NULO##NULO#30000064759PARTIDO ISOLADOPSD1BRASILEIRA NATAMA-3PIO XII15/09/197842.0304154411632MASCULINO6ENSINO MÉDIO COMPLETO1SOLTEIRO(A)2PRETA536TAXISTA12307.755SUPLENTENS-160008780202060300112DEFERIDO2DEFERIDOSIMVálido2DeferidoS
306/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALAP6106SERRA DO NAVIO13VEREADOR3000126226570233YAGO NORATO SALES DA SILVAYAGO SALES#NULO#3049609265NÃO DIVULGÁVEL12APTO4INDEFERIDO COM RECURSOPARTIDO ISOLADO70AVANTEAVANTE-1#NULO##NULO##NULO#30000165377PARTIDO ISOLADOAVANTE1BRASILEIRA NATAAP-3SERRA DO NAVIO25/06/199624.063258325182MASCULINO8SUPERIOR COMPLETO1SOLTEIRO(A)1BRANCA101ENGENHEIRO12307.754NÃO ELEITONS-160026614202060300114INDEFERIDO COM RECURSO4INDEFERIDO COM RECURSOSIMAnulado sub judice4Indeferido com recursoS
406/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALAP6017AMAPÁ13VEREADOR3000069823512111MAURÍCIO DE OLIVEIRA SUCUPIRAMAURÍCIO SUCUPIRA#NULO#63050650206NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO12PDTPARTIDO DEMOCRÁTICO TRABALHISTA-1#NULO##NULO##NULO#30000064728PARTIDO ISOLADOPDT1BRASILEIRA NATAAP-3AMAPÁ15/10/197842.014411225422MASCULINO6ENSINO MÉDIO COMPLETO1SOLTEIRO(A)3PARDA169COMERCIANTE12307.752ELEITO POR QPNS-160013217202060300012DEFERIDO2DEFERIDOSIMVálido2DeferidoS
506/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1445Eleições Municipais 2020 - AP06/12/2020MUNICIPALAP6050MACAPÁ13VEREADOR3000097717017222ADRIANO DOS SANTOS SILVAADRIANO SILVA#NULO#1130299260NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO17PSLPARTIDO SOCIAL LIBERAL-1#NULO##NULO##NULO#30000113975PARTIDO ISOLADOPSL1BRASILEIRA NATAMA-3IMPERATRIZ06/03/199525.0683323913412MASCULINO8SUPERIOR COMPLETO1SOLTEIRO(A)3PARDA257EMPRESÁRIO151743.765SUPLENTENS-160064033202060300102DEFERIDO2DEFERIDOSIMVálido2DeferidoS
806/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALAP6025PORTO GRANDE13VEREADOR3000064042518111ELIVELTON MEIRELES ARANHAELIVELTON ARANHA#NULO#1457747243NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO18REDEREDE SUSTENTABILIDADE-1#NULO##NULO##NULO#30000056130PARTIDO ISOLADOREDE1BRASILEIRA NATAAP-3PORTO GRANDE20/12/199129.057394025342MASCULINO8SUPERIOR COMPLETO1SOLTEIRO(A)3PARDA266PROFESSOR DE ENSINO MÉDIO12307.754NÃO ELEITONS-160009809202060300122DEFERIDO2DEFERIDOSIMVálido2DeferidoS
906/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1445Eleições Municipais 2020 - AP06/12/2020MUNICIPALAP6050MACAPÁ13VEREADOR3000097718617007JESUE GONCALVES DA SILVA WETCHJOSUÉ WETCH ZULU#NULO#75787610210NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO17PSLPARTIDO SOCIAL LIBERAL-1#NULO##NULO##NULO#30000113975PARTIDO ISOLADOPSL1BRASILEIRA NATAAP-3MACAPÁ21/02/198139.033684525932MASCULINO5ENSINO MÉDIO INCOMPLETO3CASADO(A)3PARDA257EMPRESÁRIO151743.765SUPLENTENN-160080228202060300102DEFERIDO2DEFERIDOSIMVálido2DeferidoS
1006/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALAP6017AMAPÁ13VEREADOR3000086338225555FRANCISCA MORAIS DA SILVALALALA#NULO#87233576220NÃO DIVULGÁVEL12APTO4INDEFERIDO COM RECURSOPARTIDO ISOLADO25DEMDEMOCRATAS-1#NULO##NULO##NULO#30000094620PARTIDO ISOLADODEM1BRASILEIRA NATAAP-3AMAPÁ27/09/197545.012008725934FEMININO3ENSINO FUNDAMENTAL INCOMPLETO1SOLTEIRO(A)3PARDA581DONA DE CASA12307.754NÃO ELEITONS-160020841202060300014INDEFERIDO COM RECURSO4INDEFERIDO COM RECURSOSIMAnulado sub judice4Indeferido com recursoS
1106/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALAP6157SANTANA13VEREADOR3000126710822800CINTIA GLAUPP LIMA DOS SANTOS BANDEIRACINTIA BRANDEIRA#NULO#501977201NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO22PLPARTIDO LIBERAL-1#NULO##NULO##NULO#30000111721PARTIDO ISOLADOPL1BRASILEIRA NATAAP-3SANTANA28/08/199030.053115325504FEMININO8SUPERIOR COMPLETO3CASADO(A)1BRANCA132PSICÓLOGO67946.235SUPLENTENS-160044390202060300062DEFERIDO2DEFERIDOSIMVálido2DeferidoS
DT_GERACAOHH_GERACAOANO_ELEICAOCD_TIPO_ELEICAONM_TIPO_ELEICAONR_TURNOCD_ELEICAODS_ELEICAODT_ELEICAOTP_ABRANGENCIASG_UFSG_UENM_UECD_CARGODS_CARGOSQ_CANDIDATONR_CANDIDATONM_CANDIDATONM_URNA_CANDIDATONM_SOCIAL_CANDIDATONR_CPF_CANDIDATONM_EMAILCD_SITUACAO_CANDIDATURADS_SITUACAO_CANDIDATURACD_DETALHE_SITUACAO_CANDDS_DETALHE_SITUACAO_CANDTP_AGREMIACAONR_PARTIDOSG_PARTIDONM_PARTIDONR_FEDERACAONM_FEDERACAOSG_FEDERACAODS_COMPOSICAO_FEDERACAOSQ_COLIGACAONM_COLIGACAODS_COMPOSICAO_COLIGACAOCD_NACIONALIDADEDS_NACIONALIDADESG_UF_NASCIMENTOCD_MUNICIPIO_NASCIMENTONM_MUNICIPIO_NASCIMENTODT_NASCIMENTONR_IDADE_DATA_POSSENR_TITULO_ELEITORAL_CANDIDATOCD_GENERODS_GENEROCD_GRAU_INSTRUCAODS_GRAU_INSTRUCAOCD_ESTADO_CIVILDS_ESTADO_CIVILCD_COR_RACADS_COR_RACACD_OCUPACAODS_OCUPACAOVR_DESPESA_MAX_CAMPANHACD_SIT_TOT_TURNODS_SIT_TOT_TURNOST_REELEICAOST_DECLARAR_BENSNR_PROTOCOLO_CANDIDATURANR_PROCESSOCD_SITUACAO_CANDIDATO_PLEITODS_SITUACAO_CANDIDATO_PLEITOCD_SITUACAO_CANDIDATO_URNADS_SITUACAO_CANDIDATO_URNAST_CANDIDATO_INSERIDO_URNANM_TIPO_DESTINACAO_VOTOSCD_SITUACAO_CANDIDATO_TOTDS_SITUACAO_CANDIDATO_TOTST_PREST_CONTAS
2614206/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPA5754URUARÁ13VEREADOR14000109704525185CRISTIANE MARIA DA SILVACRISTIANE MARIA D SILVA#NULO#90119835487NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO25DEMDEMOCRATAS-1#NULO##NULO##NULO#140000132897PARTIDO ISOLADODEM1BRASILEIRA NATAPE-3CARUARU08/05/197743.0465405508504FEMININO2LÊ E ESCREVE3CASADO(A)3PARDA257EMPRESÁRIO19052.474NÃO ELEITONS-160009541202061400792DEFERIDO2DEFERIDOSIMVálido2DeferidoS
2614306/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPA5037OEIRAS DO PARÁ13VEREADOR14000064223420234ADEMAR SERRAO FARIASADEMAR FARIAS#NULO#1810501296NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO20PSCPARTIDO SOCIAL CRISTÃO-1#NULO##NULO##NULO#140000056735PARTIDO ISOLADOPSC1BRASILEIRA NATAPA-3OEIRAS DO PARÁ07/09/198832.0513190513502MASCULINO7SUPERIOR INCOMPLETO1SOLTEIRO(A)3PARDA931ESTUDANTE, BOLSISTA, ESTAGIÁRIO E ASSEMELHADOS42582.545SUPLENTENS-160003136202061400452DEFERIDO2DEFERIDOSIMVálido2DeferidoS
2614406/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPA4154ANANINDEUA13VEREADOR14000078188145045CRISTIANE BRITO CANAVARROCRISTIANE CANAVARRO#NULO#86552805268NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO45PSDBPARTIDO DA SOCIAL DEMOCRACIA BRASILEIRA-1#NULO##NULO##NULO#140000075986PARTIDO ISOLADOPSDB1BRASILEIRA NATAPA-3BELÉM04/11/197941.0389381313844FEMININO8SUPERIOR COMPLETO3CASADO(A)3PARDA230PEDAGOGO115386.045SUPLENTENS-160028962202061400722DEFERIDO2DEFERIDOSIMVálido2DeferidoS
2614506/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPA5258SALVATERRA13VEREADOR14000073688522444FERNANDA MONTEIRO BARRETOFERNANDA MONTEIRO#NULO#70257256253NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO22PLPARTIDO LIBERAL-1#NULO##NULO##NULO#140000069238PARTIDO ISOLADOPL1BRASILEIRA NATAPA-3ABAETETUBA18/05/197941.0415498613414FEMININO6ENSINO MÉDIO COMPLETO1SOLTEIRO(A)1BRANCA999OUTROS12307.754NÃO ELEITONS-160011396202061400032DEFERIDO2DEFERIDOSIMVálido2DeferidoS
2614606/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPA5614TUCURUÍ13VEREADOR14000080470945789JOSÉ DE RIBAMAR COSTA SOUZAPROFESSOR RIBAMAR#NULO#57307245272NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO45PSDBPARTIDO DA SOCIAL DEMOCRACIA BRASILEIRA-1#NULO##NULO##NULO#140000079539PARTIDO ISOLADOPSDB1BRASILEIRA NATAMA-3CHAPADINHA28/01/197644.0284725613092MASCULINO8SUPERIOR COMPLETO3CASADO(A)1BRANCA266PROFESSOR DE ENSINO MÉDIO77517.415SUPLENTENS-160029908202061400402DEFERIDO2DEFERIDOSIMVálido2DeferidoS
2614706/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPA4081TUCUMÃ13VEREADOR14000083253814100RENILDO FERREIRAFERREIRINHA#NULO#45156069215NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO14PTBPARTIDO TRABALHISTA BRASILEIRO-1#NULO##NULO##NULO#140000087964PARTIDO ISOLADOPTB1BRASILEIRA NATAGO-3SÃO MIGUEL DO ARAGUAIA13/12/197446.0316488013092MASCULINO3ENSINO FUNDAMENTAL INCOMPLETO3CASADO(A)2PRETA999OUTROS130384.605SUPLENTENS-160023068202061400742DEFERIDO2DEFERIDOSIMVálido2DeferidoS
2614806/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPA4707CANAÃ DOS CARAJÁS13VEREADOR14000122585912456MATEUS PEREIRA XAVIERMATEUSÃO#NULO#66382378291NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO12PDTPARTIDO DEMOCRÁTICO TRABALHISTA-1#NULO##NULO##NULO#140000151278PARTIDO ISOLADOPDT1BRASILEIRA NATAMG-3SANTO ANTÔNIO DO JACINTO30/04/198040.0347682313842MASCULINO6ENSINO MÉDIO COMPLETO1SOLTEIRO(A)3PARDA602PECUARISTA87466.105SUPLENTENS-160037354202061400752DEFERIDO2DEFERIDOSIMVálido2DeferidoS
2614906/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPA5312SANTA MARIA DO PARÁ13VEREADOR14000088906615123ADRIANA DA SILVA CUNHAADRIANA ENFERMEIRA#NULO#78764602249NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO15MDBMOVIMENTO DEMOCRÁTICO BRASILEIRO-1#NULO##NULO##NULO#140000099899PARTIDO ISOLADOMDB1BRASILEIRA NATAPA-3CASTANHAL22/01/198436.0441829313764FEMININO8SUPERIOR COMPLETO3CASADO(A)3PARDA113ENFERMEIRO30015.815SUPLENTENS-160017281202061400042DEFERIDO2DEFERIDOSIMVálido2DeferidoS
2615006/05/202308:21:5320202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPA5053ORIXIMINÁ13VEREADOR14000123378920002TAMARA FERREIRA DE ALMEIDATAMARA ALMEIDA#NULO#70121693201NÃO DIVULGÁVEL12APTO2DEFERIDOPARTIDO ISOLADO20PSCPARTIDO SOCIAL CRISTÃO-1#NULO##NULO##NULO#140000152514PARTIDO ISOLADOPSC1BRASILEIRA NATAPA-3ORIXIMINÁ21/06/199822.0751121413094FEMININO6ENSINO MÉDIO COMPLETO1SOLTEIRO(A)3PARDA999OUTROS45737.835SUPLENTENN-160024293202061400382DEFERIDO2DEFERIDOSIMVálido2DeferidoS
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